System and method for scheduling broadcast of and access to video programs and other data using customer profiles

ABSTRACT

A system and method for scheduling the receipt of desired movies and other forms of data from a network which simultaneously distributes many sources of such data to many customers, as in a cable television system. Customer profiles are developed for the recipient describing how important certain characteristics of the broadcast video program, movie or other data are to each customer. From these profiles, an “agreement matrix” is calculated by comparing the recipient&#39;s profiles to the actual profiles of the characteristics of the available video programs, movies, or other data. The agreement matrix thus characterizes the attractiveness of each video program, movie, or other data to each prospective customer. “Virtual” channels are generated from the agreement matrix to produce a series of video or data programming which will provide the greatest satisfaction to each customer. Feedback paths are also provided so that the customer&#39;s profiles and/or the profiles of the video programs or other data may be modified to reflect actual usage. Kiosks are also developed which assist customers in the selection of videos, music, books, and the like in accordance with the customer&#39;s objective profiles.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a system and method for controllingbroadcast of and/or customer access to data such as video programs inaccordance with objective profile data indicative of the customer'spreferences for that data. More particularly, a preferred embodiment ofthe invention relates to a system and method for determining fromobjective profile data of the customers which data or video programmingis most desired by each customer so that the customers may receive dataor video programming customized to their objective preferences. Theobjective profile data is updated on a continuing basis to reflect eachcustomer's changing preferences so that the content of the data channelsor video programming may be updated accordingly.

2. Description of the Prior Art

The so-called “Information Super Highway” is expected to bring wondroustechnological changes to society. Data of all kinds will become readilyavailable to the public in quantities never before imaginable. Recentbreakthroughs in video compression technologies are expected to extendthe “Information Super Highway” right into the video realm by allowingcustomers to receive literally hundreds of video channels in theirhomes. While the prospects of opening a whole new world of informationto the average person are exciting, there is much concern that theaverage person will simply be overwhelmed by the quantity of data pipedinto their homes. Some techniques must be developed which permit thetravelers of the Information Super Highway to navigate through theplethora of available information sources without getting hopelesslylost.

For example, in the home video context, it is desired to providemechanisms which present the available video information to thecustomers in a comprehensible way. Such mechanisms should eliminate thenecessity of “channel surfing” to find a program suitable for viewingout of the hundreds of video programming alternatives which are expectedto be made available. The present invention is thus designed to help thecustomer of video and other data services to receive, with minimaleffort, the information he or she is most interested in.

Numerous systems are available which assist customers in determiningwhich video programs to watch. For example, electronic program guidesand the like are available which give customers on-screen access to theupcoming programming whereby the desired programming may be selected inadvance for later recording. An early system described in U.S. Pat. No.4,170,782 to Miller allows the viewer to preselect a television viewingschedule of desired television channels to be viewed during subsequenttime periods. Miller also monitors the television programs actuallywatched by the television viewer and relays this information to acentral data processing center over a communication link. Subsequentinteractive cable systems, such as that described by Freeman in U.S.Pat. No. 4,264,924, permit the viewer to select the information to bereceived on particular channels. The cable system described by Freemanalso provides individually tailored messages to the individual viewers.Similarly, Young disclosed in U.S. Pat. No. 4,706,121 a system whichpermits the viewer to select programs from schedule information bycontrolling a programmable television tuner to provide the broadcastsignals for the selected programs to the television receiver at the timeof broadcast. This system can also be used to control a VCR forunattended recording of the selected programs. Further details of such aVCR recording system is provided by Young in U.S. Pat. Nos. 4,977,455and 5,151,789. Other systems, such as that described by Reiter et al. inU.S. Pat. No. 4,751,578, provide updatable television programminginformation via telephone link, magnetic cards, floppy disks, televisionor radio sub-carrier, and the like, to the viewer's television screen insuch a manner that the viewer may selectively search this information.

Unfortunately, in each of the aforementioned prior art systems, thecustomer must actively select the desired programming. In other words,these systems facilitate access to programming designated by thecustomer but provide no assistance to the customer in determining whatprogramming to select for subsequent viewing. With the possibility ofseveral hundred video channels soon becoming available to videocustomers, additional systems are desirable which assist the customer inselecting the desired programming.

The system described by Herz et al. in U.S. Pat. No. 5,351,075 partiallyaddresses the above problems, at least with respect to the provision ofmovies over cable television. As described therein, members of a “HomeVideo Club” select the video programs they would like to see in thefollowing week. A scheduling computer receives the members' inputs forthe current week and determines the schedule for the following weekbased upon the tabulated preferences. This schedule is then madeavailable to the members of the Home Video Club. If, when, and how oftena particular video program is transmitted is determined by the customerpreferences received by the scheduling computer. Prime time viewingperiods are used to make certain that the most popular video programsare broadcast frequently and at the most desirable times. As with theaforementioned systems, the “Home Video Club” system does notautomatically broadcast the most desired video programs to the customersbut instead requires the active participation of the customers to “vote”for the most desired video programs for subsequent viewing.

It is desired to extend a customer preference system such as the “HomeVideo Club” to include general cable programming offerings and tominimize active customer involvement in the determination of the desiredprogramming. Unlike the movie scheduling system described in the “HomeVideo Club” application, the number and content of general cableprogramming channels is scheduled in advance and typically cannot bechanged by the customer through a simple voting system. As a result, thecustomer can only vary his or her video programming by changingchannels. In other words, the customer typically illustrates his or herprogramming preferences by changing channels. Indeed, such changes aremonitored by Nielsen, Arbitron, and other ratings agencies in settingthe rates for advertising. In U.S. Pat. No. 5,155,591, one of thepresent inventors carried this concept a step further by obtaininginformation about the customers and then demographically targetingtelevision commercials to the customers most likely to respond favorablyto such advertising. Unfortunately, however, this demographic andcustomer preference information has not been specifically described forproviding customized channels which better reflect the customers'preferences for the programming itself.

The present inventors have found that the afore-mentioned problems maybe overcome by creating customized programming channels from all of theprogramming available at any time and broadcasting the customizedprogramming channels to groups of customers. The customer's set topmultimedia terminal then creates “virtual channels” as a collection ofthe received programming data from one or more of the customizedprogramming channels at any point in time for receipt on the customer'stelevision. These virtual channels are received as an additionaloffering to the regular broadcast transmission and are customized to thecustomer's preferences. Thus, as used herein, a “virtual channel” is achannel formed as a composite of several source materials or programswhich may or may not change during respective time periods to reflectthe programming most desirable to the customer during that time period.The creation of such “virtual channels” is intended to minimize theamount of “channel surfing” necessary to find the most preferred videoprogram at a particular time.

Previous attempts at providing such selective access to programming haverequired active customer participation. For example, in U.S. Pat. No.4,381,522, Lambert disclosed a system in which the customer is permittedto specify which television signal source is to be connected to thevideo switch for broadcasting of a desired television program to thecustomer. The desired program is selected from a program schedulechannel provided to the customer. Hashimoto discloses more elaboratesystems in U.S. Pat. Nos. 4,745,549 and 5,075,771 in which programssuitable to individual customer's tastes are selected from all of theavailable television programs in accordance with the customerpreferences specified on a customer questionnaire or provided from thecustomer over a telephone link or the like. The viewer preference dataprovided using the questionnaires, the telephone lines, and the like isthen statistically processed by linear programming to provide anindividual subscriber television program list which may be used by thevideo provider to select which programs to broadcast to particularindividuals. Subscriber complaints about the program list are used to“tune” the television program list to better match the individual'stastes. An automatic controller is also used to automatically control atelevision or video cassette recorder in accordance with thesubscriber's specified tastes. However, the system disclosed byHashimoto works from limited objective data provided by the customer inresponse to a questionnaire and provides no mechanism for validating theaccuracy of the profile of that customer other than through the use of acomplaint system. In addition, the system disclosed by Hashimoto doesnot determine the desirability of particular video programs but merelyallows the customer to characterize those types of programs to which heor she may be most interested.

For the reasons noted above, feedback regarding the customer programmingand purchasing preferences is highly desirable. It is highly desirableto develop a technique for better acquiring and quantifying suchcustomer video programming and purchasing preferences. Along theselines, Strubbe recently described a system in U.S. Pat. No. 5,223,924which provides an interface for automatically correlating the customerpreferences with the television program information and then creatingand displaying a personalized customer program database from the resultsof the correlation. In the Strubbe system, the customer specifieswhether he or she “likes” a particular video program and the database isupdated accordingly. Then, from the video programs “liked” by thecustomer, a second, personalized, database is created. However, as witheach of the systems described above, the Strubbe system does not developcustomer profiles and automatically update the database of “liked”videos using feedback. Also, Strubbe does not teach that the preferenceinformation may be used to predict what new video programs the customermay like and then schedule those new video programs for viewing.

Those in the technical press have fantasized about so-called “smart”televisions which will keep track of past viewing preferences andsuggest new programs that match the customer's personal tastes so thatthe customer need not “channel surf” through the 500 channel videosystem of the future. However, prior to the present invention, no oneknown to the present inventors has been able to make such “smart”televisions a reality. Indeed, the present invention is believed to bethe first system to create “virtual channels” of recommended programmingfor each customer of a video or other data service.

SUMMARY OF THE INVENTION

The present invention relates to a system and method for makingavailable the video programming and other data most desired by thecustomer by developing an “agreement matrix” characterizing theattractiveness of each available source of video programming or data toeach customer. From the agreement matrix, one or more “virtual channels”of data, customized to each customer, are determined. At any given time,the one or more virtual channels include the video programming or otherdata which is predicted to be most desirable to the customer based onthe customer's preferences. The virtual channels are determined byselecting from the available alternatives only those video programs orother data which most closely match the customer's objectivepreferences.

In accordance with the invention, a method of scheduling customer accessto data from a plurality of data sources is provided. Although thetechnique of the invention may be applied to match customer profiles forsuch disparate uses as computerized text retrieval, music and musicvideo selection, home shopping selections, infomercials, and the like,in the presently preferred embodiment, the method of the invention isused for scheduling customer access to video programs and otherbroadcast data. In accordance with the preferred method, objectivecustomer preference profiles are obtained and compared with contentprofiles of the available video programming. The initial customerprofiles are determined from customer questionnaires, customerdemographics, relevance feedback techniques, default profiles, and thelike, while the initial content profiles are determined fromquestionnaires completed by “experts” or some sort of customer's panel,are generated from the text of the video programs themselves, and/or aredetermined by adopting the average of the profiles of those customerswho actually watch the video program. Based on the comparison results,one or more customized programming channels are created fortransmission, and from those channels, each customer's set topmultimedia terminal may further determine “virtual channels” containinga collection of only those video programs having content profiles whichbest match the customer's profile and hence are most desirable to thecustomer during the relevant time frame.

Preferably, one or more customer profiles are created for each customerof the video programs. These customer profiles indicate the customer'spreferences for predetermined characteristics of the video programs andmay vary in accordance with time of day, time of the week, and/orcustomer mood. Such “characteristics” may include any descriptivefeature suitable in describing particular video programs, such asclassification category; directors; actors and actresses; degree of sexand/or violence; and the like. Corresponding content profiles arecreated for each video program available for viewing and generallyindicate the degree of content of the predetermined characteristics ineach video program. An agreement matrix relating the customer profileswith the content profiles is then generated. Preferably, the agreementmatrix enables the system to determine a subset of the availableprograms at a particular point in time which is most desirable forviewing by the customer. The determined subset of video programs is thenpresented to the customer for selection in the conventional manner,except that each “virtual channel” includes a collection of theofferings available on all of the originally broadcast channels from thecable system. The “virtual channels” are then generated by thecustomer's set top multimedia terminal for display on the customer'stelevision. The customer may then select the desired video programming,which may or may not include the programming offered on the “virtualchannels.” Similar techniques are used at the video head end todetermine which video programs to transmit to each node for use in thecreation of the “virtual channels” at each customer's set top multimediaterminal.

Preferably, the customer profile creating step comprises the step ofcreating a plurality of customer profiles for each customer, where theplurality of customer profiles are representative of the customer'schanging preferences for the predetermined characteristics in accordancewith time of the day and of the week. In such an embodiment, theagreement matrix determining step comprises the step of using differentcustomer profiles for each customer in accordance with the time of theday and of the week, thereby reflecting changes in the customer'spreferences or “moods” during the course of the week. In addition, thecustomer profile creating step preferably comprises the step ofclustering customer profiles for combinations of customers expected toview the video programs at a particular customer location at particulartimes on particular days. For example, the clustered profiles for acustomer's residence may contain the combined profiles of Mom and Dad inthe evening and the combined profiles of the children in the afternoon.In this embodiment, the agreement matrix determining step comprises thestep of using the different clustered customer profiles in accordancewith the time of the day and of the week. Alternatively, the appropriatecustomer profiles for use in calculating the agreement matrix may bedetermined directly from identity information received from the customeror assigned to the customer in accordance to the cluster of customers towhich that customer belongs. In the latter technique, it will beappreciated that customer profiles are not strictly necessary since eachcustomer is assigned an initial customer profile determined from theclustered profiles of the other customers in his or her cluster ofcustomers.

In the presently preferred embodiment of the invention, the agreementmatrix determining step comprises the step of comparing the customerprofiles with the content profiles for each video program available forviewing in a predetermined time period. In particular, the agreementmatrix determining step preferably comprises the step of determining adistance in multidimensional characteristic space between a customerprofile and a content profile by calculating an agreement scalar forcommon characteristics, ac, between the customer profile, cv, and thecontent profiles, cp, in accordance with the relationship:ac _(ij)=1/[1+Σ_(k) w _(ik) |cv _(ik) −cp _(jk)|],for i=a particular customer of a number of customers I, j=a particularvideo program of a number of video programs J, and k=a particular videoprogram characteristic of a number of video program characteristics K,where W_(ik) is customer i's weight of characteristic k. As will beappreciated by those skilled in the art, an agreement matrix so definedis the reciprocal of the distance d (=1/ac) in multi-dimensional spacebetween the customer profile vector and the content profile vector andthat many different distance measurement techniques may be used indetermining the distance d. In such an embodiment, the subsetdetermining step preferably comprises the steps of sorting the videoprograms in an order of ac indicating increasing correlation andselecting as the subset a predetermined number of the video programshaving the values for ac indicating the most correlation.

When scheduling video programs at a head end using the techniques of theinvention, the agreement matrix is preferably determined from customerprofiles of a plurality of customers and the video programming isscheduled using the steps of:

(a) determining a video program j which most closely matches thecustomer profiles of the plurality of customers of the video programs;

(b) scheduling the video program j for receipt by the plurality ofcustomers and decrementing a number of channels available fortransmission of video programs to said customers;

(c) when the number of channels available for transmission of videoprograms to a particular customer of the plurality of customers reacheszero, removing the particular customer from the plurality of customersfor scheduling purposes; and

(d) repeating steps (a)-(c) until the number of video programs scheduledfor transmission equals the number of channels available fortransmission of video programs.

In accordance with a currently preferred embodiment of the invention, apassive feedback technique is provided for updating the customerprofiles in accordance with the video programming actually watched bythe customer. Such a method in accordance with the invention preferablycomprises the steps of:

creating at least one customer profile for each customer of the videoprograms, the customer profile indicating the customer's preferences forpredetermined characteristics of the video programs;

creating content profiles for each video program available for viewing,the content profiles indicating the degree of content of thepredetermined characteristics in each video program;

monitoring which video programs are actually watched by each customer;and

updating each customer profile in accordance with the content profilesof the video programs actually watched by that customer to update eachcustomer's actual preferences for the predetermined characteristics.

Preferably, the monitoring function is accomplished by storing, at eachcustomer's set top multimedia terminal, a record of the video programsactually watched by the customer at the customer's location and, in thecase of a system with a two-way communication path to the head end,polling the set top multimedia terminals of all customers to retrievethe records of the video programs actually watched by the customers ateach customer location. Also, from the retrieved records, combinedcustomer profiles may be determined which reflect the customer profilesof a plurality of customers. Then, by determining the agreement matrixusing the combined customer profiles for each node, programming channelscontaining the video programming which are collectively most desired bythe customers making up the combined customer profiles may be determinedfor transmission from the head end to each of the customers connected tothe same node.

When a predicted video program is not selected by the customer, it isdesirable to update the agreement matrix to better reflect thecustomer's tastes. The updating of the agreement matrix may beaccomplished in a variety of ways. For example, the customer profile,cv_(ik), for customer i and video program characteristic k may beadjusted to a new customer profile, cv_(ik)′, in accordance with theequation:cv _(ik) ′=cv _(ik)−Δ(cv _(ik) −cp _(jk)),where cp_(jk) represents the degree of video program characteristic k invideo program j and Δ is a small constant which can vary in accordancewith the desired accuracy for the profiles. On the other hand, customeri's weighting of video program characteristic k, w_(ik), in the customerprofile, cv_(ik) may be adjusted to a new weighting, w_(ik)′, inaccordance with the equation:w _(ik)′=(w _(ik) −Δ|cv _(ik) −cp _(jk)|)/Σ_(k)(w _(ik) −Δ|cv _(ik) −cp_(jk)|).In addition, the content profiles, cp_(jk), of certain video programs jhaving video program characteristics k may be adjusted to new contentprofiles, cp_(jk)′, to update the customer profiles of customers i whoactually watch video program j, in accordance with the equation:cp _(jk) ′=cp _(jk)−Δ(cp _(ik) −cp _(jk)),where cv_(ik) represents the customer profile of customer i for videoprogram characteristic k. Of course, other updating techniques are alsopossible within the scope of the invention.

Since the data passing from the set top multimedia terminal to the headend contains data which the customers may consider to be confidential,the two-way transmission system of the invention may be modified toencrypt the transmissions from the set top multimedia terminals to thehead end. Similarly, as in the case of pay-per-view programming, it isoften desirable to encrypt the transmissions from the head end to theset top multimedia terminals. In accordance with the invention, a securetransmission system from the head end to the set top multimedia terminalis obtained by performing the steps of:

(1) At the set top multimedia terminal, generate a seed random number Nto be used for the random number generator.

(2) Retrieve the public key P from the head end and encrypt the seedrandom number N as E(N,P) at the set top multimedia terminal using apublic key algorithm such as RSA which is known to be difficult tobreak.

(3). Send the encrypted seed N (E(N,P)) to the head end where E(N,P) isreceived and decrypted to yield N using the head end's private key Q.

(4) The head end and set top multimedia terminals then initialize theirrespective pseudo-random number generators with N as a seed.

(5) Begin the encryption at the head end by generating the first numberin the sequence K_(i) and logically exclusive-ORing it with the firstdata word in the stream P_(i), thereby forming C_(i) (i.e.,C_(i)=EOR(K_(i), P_(i))).

(6) Send the result C_(i) from the encryptor at the head end to the settop.

(7) Form K_(i) at the synchronized random number generator of the settop multimedia terminal, which has also been initialized with seed N, bydecrypting the received C_(i) to yield P_(i). This is done byexclusive-ORing K_(i) with C_(i) to yield P_(i) (i.e., P_(i)=EOR(K_(i),C_(i))), generating the next pseudo-random K_(i) in the sequence at thehead end and the set top multimedia terminal, determining whether allwords i in the sequence have been decrypted, and repeating steps (5)-(6)until all words in the digital video stream have been decrypted. Normalprocessing of the digital video stream continues from that point. Securetransmission from the set top multimedia terminal to the head end isobtained in the same manner by reversing the set top multimedia terminaland the head end in steps (1)-(7) above.

Those skilled in the art will appreciate that the techniques describedherein are applicable to numerous other areas of technology in which itis desirable to assist the customer in the selection of a data servicewhich best meets that customer's needs. For example, the agreementmatrix of the invention may be used to facilitate text retrieval in acomputer database system and may be implemented in a kiosk or personalcomputer designed to assist in the selection of movies, music, books,and the like. All such embodiments will become apparent to those skilledin the art from the following description of the preferred embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects and advantages of the invention will becomemore apparent and more readily appreciated from the following detaileddescription of the presently preferred exemplary embodiments of theinvention taken in conjunction with the accompanying drawings, of which:

FIG. 1 is a flow chart illustrating the flow of processing of thecustomer and content profile data in accordance with a preferredembodiment of the invention.

FIG. 2 is a flow chart illustrating the method of selecting the contentsof channels which are to be transmitted from a CATV head end to aplurality of customers in accordance with the invention.

FIG. 3 is a flow chart illustrating the method of passively updatingcustomer profiles in accordance with the invention.

FIG. 4 is a generalized system overview of a one-way customer profilesystem in accordance with the invention in which customized virtualchannels are created at the set top multimedia terminals from thechannels received from the CATV head end.

FIG. 5 is a generalized system overview of a two-way customer profilesystem which expands upon the embodiment of FIG. 4 by feeding back datarepresentative of the customers' viewing habits from the customers' settop multimedia terminals to the CATV head end for purposes of optimallyscheduling the channels for transmission from the head end in accordancewith the recorded customer preferences.

FIG. 6 is a block diagram of a cable television distribution system,including an optional two-way return path, which has been modified totransmit video programs determined from the fed back customer profiledata in accordance with the techniques of the invention.

FIG. 7 is a flow diagram of an upstream encryption technique forencrypting data sent from the set top multimedia terminal to the headend in accordance with the techniques of the invention.

FIG. 8 is a flow diagram of a downstream encryption technique forencrypting data sent from the head end to the set top multimediaterminal in accordance with the techniques of the invention.

FIG. 9 is a block diagram of the software used in the set top multimediaterminals in a preferred embodiment of the invention.

FIG. 10 is a block diagram of a preferred hardware implementation of aset top multimedia terminal in accordance with the invention.

FIG. 11 is a simplified block diagram of a computer kiosk or personalcomputer which uses the profile and clustering techniques of theinvention to assist a customer in the selection of videos for rental,music or books for purchase, and the like.

DETAILED DESCRIPTION OF THE PRESENTLY PREFERRED EMBODIMENTS

The present invention will be described in detail below with respect toFIGS. 1-11. Those skilled in the art will appreciate that thedescription given herein is for explanatory purposes only and is notintended to limit the scope of the invention. For example, while theinvention is described with respect to a cable television broadcastingsystem, those skilled in the art will appreciate that the systemdescribed herein may also be used for selecting receipt of desired dataservices and shop at home services, and for selecting from availablemusic and multimedia offerings. Accordingly, the scope of the inventionis only to be limited by the scope of the appended claims.

I. Overview

The present invention relates to a customer profile system in which thecharacteristics of a data source are quantified in some objective mannerand stored as content profiles and the customer's preferences for thosecharacteristics are stored in the form of one or more customer profiles.In the following detailed description, the present inventors willdescribe how the techniques of the invention are used for creatingcontent profiles which characterize the data sources in accordance withtheir degree of content of predetermined characteristics. Techniqueswill also be described for creating, weighting, and updating customerprofiles which reflect the customer's affinity for those predeterminedcharacteristics. From the content profiles and the customer profiles, anagreement matrix will be described which matches the customers'preferences and the contents of the data sources available at any pointin time. As will be described in detail below, the agreement matrix isused at the customer's set top multimedia terminal to create “virtual”data channels from the available data sources, or, alternatively, theagreement matrix may be used by the data provider to determine whichdata sources of those available will have the most appeal to his or hercustomers.

A preferred embodiment of the invention will be described in the contextof a one-way CATV transmission system and a two-way transmission CATVtransmission system with feedback for adjusting the agreement matrix.Several of the numerous possible alternative embodiments for applicationof the techniques of the invention will then be described.

II. Content Profiles and Customer Profiles

As noted above, a preferred embodiment of the invention will bedescribed for application in a CATV distribution system for aiding acustomer in the selection of video programming for viewing by matchingthe available video programming to each customer's objectivepreferences. Accordingly, the content and customer profiles will includecharacteristics which are useful in defining the characteristics ofvideo programming. Of course, when the present invention is used toassist in the selection of data from other data sources, the content andcustomer profiles will include completely different characteristics.

In accordance with the preferred embodiment of the invention, thecontent profiles describe the contents of video programs and arecompared mathematically in a computer to customer profiles to generatean agreement matrix which establishes the degree of correlation betweenthe preferences of the customer or customers and the video programmingavailable during each video programming time slot. The content profilesand the customer profiles are thus described as a collection ofmathematical values representing the weighted significance of severalpredetermined characteristics of the video programming. For ease ofdescription, the present inventors will describe the mathematical basisfor the content profiles and the customer profiles in this section andwill describe the generation of the agreement matrix and the uses of theagreement matrix in the next section.

A. Terminology

The following subscription indices will be used throughout thisspecification: i customers (i = 1,2, . . . ,I); j programs (j = i,2, . .. ,J); k characteristics (k = 1,2, . . . ,K); l categories (l = 1,2, . .. ,L);and the following variables will be used throughout this specification:

-   cv_(ik): customer i's rating for characteristic k;-   CV_(i): the vector {cv_(ik)|kεK} which forms customer i's profile    for all characteristics k;-   sv_(ik): spread (flexibility) in viewer i's rating for    characteristic k;-   wv_(ik): customer i's weight of characteristic k;-   cp_(jk): objective weighting of program j for characteristic k;-   CP_(j): the vector {cp_(jk)|kεK} which forms program j's profile for    all characteristics k;-   sp_(jk): spread (flexibility) in program j's rating for    characteristic k; and-   ac_(ij): agreement scalar representing similarity between CV_(i) and    CP_(j).

It should be noted at the outset that cv_(ik) indicates customer i'spreferred level for characteristic k, while cp_(jk) indicates the levelof presence of a characteristic in the program. sv_(ik) and sp_(jk), onthe other hand, respectively represent customer i's flexibility inaccepting different levels of characteristic k and the flexibility inthe determination of the degree of content of characteristic k inprogram j.

cv and cp may have values between 0 and +10, where the actual rangenumber indicates the relevance of that characteristic. In other words, avideo programming having a value of +10 for a given characteristic hasthe highest degree of content for that variable. The values of cv and cpshould always be non-negative, since both are related to the level ofcharacteristics, the former being the desired level and the latter beingthe actual level. Naturally, zero in cv means the customer's rejectionof a characteristic, while zero in cp indicates the absence of acharacteristic in a program. Those skilled in the art may wish to allowcv to become negative so that the magnitude of negativity could indicatethe level of the customer's aversion to a characteristic. However, thereare drawbacks in using negative values in this manner. First, a negativevalue will blur the meaning of cv and sv and weaken the statisticalbasis for their calculation. Second, cv has been defined as the desiredlevel of a characteristic, which should be non-negative. Thus, the levelof aversion is preferably expressed as a point value, instead of a rangeof values.

Preferably, the level of aversion is expressed by the combination of azero-value in cv and a certain value in the corresponding wv. Forexample, when customer i totally rejects characteristic k, cv_(ik) canbe set to −1, which means prohibition. Any program k for which cv_(ik)<0will be excluded from the recommendation list for customer i. Of course,as in the Strubbe system, the values could simply be “0” or “1” toindicate the presence or absence of a characteristic.

wv_(ik) illustrates the importance of characteristic k to customer i.Typically, different characteristics bear different levels of importancefor a customer, and the introduction of this variable catches thevariation. Although any scaling system may be used, the weight variable,wv, may simply weight the associated characteristic on a scale of 0-5,where 5 indicates the highest affinity for the associatedcharacteristic. On the other hand, as in the Strubbe system, the weightscould simply indicate a “like” or “dislike” value for eachcharacteristic.

Finally, as will be described in more detail below, the agreement scalarfor characteristics, ac, is the weighted average of the values of avariant of the two-sample t test for significance between CV and CP.

B. Creating Initial Customer and Content Profiles

A profile, either of a customer (Customer Profile) or of a program(Content Profile), is composed of arrays of characteristics which definethe customer profile vector CV_(i) and the program profile vectorCP_(j). To increase the accuracy in statistical estimation, theselection of the characteristics should follow the following guidelines:

-   -   The characteristics should be descriptive of the features of the        programs;    -   The list should be fairly inclusive, i.e., include all the        common features of the programs; and    -   There should be no synonyms, nor much overlapping in meaning        between two or more characteristics. In other words, the        correlations between the characteristics are desirably        minimized.

For example, characteristics currently in use for characterizing videoprogramming include film genres such as westerns, comedies, dramas,foreign language, etc. as defined by the American Film Institute and/oras provided via existing television database services; directors;actors/actresses; attributes such as amount and degree of sex, violence,and profanity; MPAA rating; country of origin; and the like. Of course,many other characteristics may be used, such as those characteristicsused in the Minnesota Psychological Test or other more particularizedcategories based on life experiences and emotions. Such characteristicsmay also be value based by indicating the scientific, socio-political,cultural, imagination evoking, or psychological content as well as thematurity level to which the program appeals.

In accordance with the invention, there are several ways to develop theinitial customer and content profiles for such characteristics. Forexample, the initial customer profile may be assigned on the basis ofthe customer's zip code or other characteristic demographic information.In other words, the profile may be set to a profile typical of thecustomer's zip code area or to a typical profile determined byinterviews or empirically by monitoring what customers watch. Similarly,each customer may be assigned a generic customer profile which ispersonalized over time through the profile adjustment techniques to bedescribed below. Alternatively, a customer may be asked to name severalof his or her favorite movies and television shows so that an initialcustomer profile may be determined by combining or averaging the contentprofiles of the selected movies and television shows. In addition, eachcustomer may complete a ballot for each viewing mood. This latterapproach builds upon the technique described by Hashimoto in U.S. Pat.Nos. 4,745,549 and 5,075,771 and will be described in some detail here.

For explanation purposes, it will be assumed that the initial customerprofile is determined from an initial customer questionnaire or ballot.When completing the initial questionnaire, the customer may choosebetween two voting schemes, one (Scheme A) by characteristics, and theother (Scheme B) by categories. Scheme A is straight-forward. In SchemeA, the customer gives acceptable ranges for all the characteristicswhich identify a video program. The customer's profile {cv|cvεCV} isimmediately obtained by simply calculating the means of these ranges. InScheme B, however, the customer gives a specific rating for each of thecategories. If ccv_(il) is the rating for customer i for category l,with a scale of 10 in which zero means least satisfaction with thecategory and 10 means the greatest satisfaction, the customer's profile{cv_(ik)|cv_(ik)εCV_(i)} may be calculated as:cv _(ik)=1/N _(L′)*Σ_(l′) cc _(kl′) (1′εL′)  Equation (1)where L′ is the set of categories in which ccv_(il)=max_(l) ccv_(il′)and N_(L′) is the cardinality of L′. In other words, a customer's ratingof a characteristic equals the objective content rating of thatcharacteristic in the customer's most preferred category. If there aremultiple most-preferred categories, indicated as ties in ccv, theobjective content ratings can be used.

Alternatively, the customer may be required to input an upper limitcvu_(ik) and a lower limit cvl_(ik) for characteristic k to show his/heracceptable range for characteristic k, where cv_(ik) is calculated as:cv _(ik)=(cvu _(ik) +cvl _(ik))/2  Equation (2)i.e., the middle point of the range. If customer i wants to indicatehis/her indifference to characteristic k, i.e., that he/she can acceptany level of characteristic k, then he/she may either let cvu_(ik)=10and cvl_(ik)=0, or let wv_(ik)=0.

The initial value for cp_(jk) is calculated as the mean of all votes onthat characteristic by “experts” or other viewers used to characterizethe video programs. As will be noted below, initial profiles for videoprograms may be obtained by using a panel of experts or customers toassign content profiles or by assigning the customer profiles to thevideo programs on the basis of those who liked the video program duringa test screening. On the other hand, the initial value for sv_(ik) iscalculated as:sv _(ik)=(cvu _(ik) −cvl _(ik))/Z,  Equation (3)where Z preferably equals 2. The calculation simulates that for standarddeviation, where the cutting point for rejection in normal distributionis usually when the divisor Z=2. Accordingly, cv is of point value, andsv is of range value. Thus, while the value of the divisor Z in Equation(3) may be altered to tighten/loosen the cutting point, the divisor inEquation (2) may not be changed. sp, on the other hand, may becalculated as the standard deviation in the experts' or test group'svotes on cp.

The major advantage of Scheme B is that much burden will be taken awayfrom the customer in the ballot completion process, since as a rule, thenumber of characteristics well exceed the number of categories. Thedisadvantage, however, is the inaccuracies due to the fact that not allthe characteristics in the customer's favorite category are on thecustomer's most preferred level. The inaccuracy may be reduced byexpanding the most-preferred categories to those with ccv values in acertain upper percentile, rather than with the maximum ccv values. Thevalue of sv can be derived from the deviation of ccv's in the customer'smost-preferred categories. To help the customers vote more consistently,each category is preferably accompanied by a list of keywords (i.e.,characteristics that are relevant to the category).

Similarly, the content profile may be determined using questionnairescompleted by a panel of experts or customers who determine the contentof all video programming available for broadcast. The same scalingsystems would be used as for the customer profiles. For statisticalpurposes, it is desired that the expert or customer panel be as large aspossible. As will be noted below, once the system of the invention is inoperation, the customer profiles of those who actually watch aparticular video program during an initial screening by a sample groupmay be used to assign a content profile to that program for subsequentviewings. In such a system, those customers who watch the program frombeginning to end will be presumed to have “liked” the program. Thecustomer profiles of those who “liked” the program would then becombined to create the initial profile for that program. That programwould then be ready for broadcast to all members of the network.

Alternatively, in the presently preferred embodiment, more sophisticatedtechniques are used to generate the initial content profiles. In thepreferred embodiment, the content profile of a program is determinedautomatically from the word frequency of certain words in the text oron-line description of a program or the frequency of certain words inthe closed captions of a television show, where such words are chosen asrepresentative of certain categories. Of course, other simplertechniques such as one which simply determines the presence or absenceof particular characteristics may be used within the scope of theinvention.

The weighting of the characteristics in the customer and contentprofiles somewhat depends on how the profiles were determined initially.For example, the weighting of the customer profiles may be obtaineddirectly from a questionnaire by asking the customer to appropriatelyscale (from 1-10) his or her preference for each characteristic. On theother hand, if the customer profile is assigned based on demographics,zip code, and the like, the average weights for other customers with thesame demographics, zip code, and the like may be used. When morestatistical techniques are used for creating the initial customer andcontent profiles, the weights may be determined mathematically as, forexample, the reciprocal of the standard deviation of thecharacteristics. Of course, other weighting techniques may also be used.

C. Adjusting Customer Profiles

As noted above, the data from which the initial customer profile isderived may be obtained through ballot filling, whereby a number ofcharacteristics are listed and the customer gives his/her preferencerating (cv) and flexibility range (sv) for each characteristic. However,people often do not provide all of the necessary responses or thecorrect responses to such ballots or questionnaires. Similarly, when theinitial customer profiles are assigned to new customers on the basis ofdemographics and the like, there is a substantial likelihood that theinitial customer profile will need considerable adjustment. Moreover,the system should account for the fact that many people's tastes changeover time. Thus, to ensure accuracy of the profiles, there must be someway to correct errors in the initial customer profiles and to adjust thecustomer profiles over time.

In accordance with the invention, a passive feedback technique isprovided whereby the programming viewed by the customers areautomatically monitored and used to adjust the customer profiles. Thattechnique will be described in more detail in Section V below. Thissection will instead refer to an active feedback mechanism which will bereferred to as a “rave review.”

As noted above, one way to establish an initial customer profile is toshow an unrated program or portion of a program to a target audience andto assign to the unrated program a combination of the customer profilesof those who actually watched the program or portion of the program frombeginning to end or to assign ratings inputted by those who completed asurvey. A similar technique may be used for error correction or forcreating initial customer profiles. In particular, the customer isexposed to a series of short sections of different video programs. Eachsection is characterized by a few characteristics, and the assignedcharacteristic level of each of the characteristics is presented to thecustomer. The customer is then asked to state his/her most preferredlevel for the characteristic given the assigned characteristic level forthe viewed section of the video program. For instance, if the level of“action” in a section of the movie “First Blood” is assigned a value of8; the customer may give 4-6 as his/her acceptance range. On the otherhand, if the customer strongly disagrees with the assignedcharacteristic level, he/she may provide his/her own estimation of thelevel of the characteristic in the presented program section and givehis/her acceptance accordingly. Of course, the major advantage of such a“rave review” procedure over ballot completion is that instead of votingon an abstract concept, the customer now makes estimations based onconcrete examples.

Since the customer may not be able to remember exactly his/her preferredlevel of a characteristic that he/she indicated in a previous review orin an original ballot or may not have known his or her assigned initialvalue for a characteristic, he/she may intentionally or involuntarilyrepeat the same level for the same characteristic in one review if thatcharacteristic appears in more than one program section, even if thatcharacteristic should not have the same level. Therefore, in a “ravereview” a program section with a large variety of characteristics shouldbe selected to avoid repetitions of the same characteristic acrossseveral video clips. In addition, the customer should be advised thatwhile making level estimations he/she should concentrate on the featuresof the current program sections and forget his/her previous ratings.

As mentioned above, changes in a customer profile should be expectedover time. In other words, the values of cv and sv obtained from therave reviews typically will be different from the values specified inthe original ballot and during previous rave reviews. In order to avoiddramatic changes in the values, the setting of the new values shouldtake into consideration both the old and the new data. Thus, if xrepresents either cv or sv, x^(n) is the value of x after period n (n“rave review” changes), and y^(n) is the value obtained during time n,then:x^(n)=y^(n) when n=1  Equation (4)andx ^(n) =x ^(n−1)+(1/n)y ^(n−1) −x ^(n−1)) when n>1. Equation (5)Each y is obtained either from the customer's ballot or rave review, buttypically y¹ is from the ballot data, and y¹, 1>1, is from the ravereview data. From the above equations, it follows that:x ^(n)=(1/n)Σ₁ y ¹,  Equation (6)i.e., the new value of x after the n iterations is equal to the averageof all the n−1 previous data. The method is formally termed MSA, orMethod of Successive Average. With this method, data at each iterationis equally accounted for in the final value. Any dramatic changes willbe damped down, especially at later iterations (when n is large). Thisapproach agrees with intuition, since a customer's profile shouldstabilize over time.

However, customers may have systematic bias in estimating theirpreferences. For instance, a customer may constantly underestimate oroverestimate his/her preference rating for a characteristic. One way todetect the inaccuracy is to check if there are some customers who, whenviewing various programs in rave reviews, constantly disagree with thecontent profile value for a particular characteristic and suggestdisproportionately higher/lower ratings. If frequently the t values inthe t significance test turn out to be insignificant despite theiragreement, then it may be necessary to adjust the customers' ratings ofthe characteristic in question in the direction opposite to theirsuggestions.

Another way to make adjustments to the customers' combined ratings isthrough the clustering of customers. Customers are asked to give overallratings for various programs. If a group of customers come up with verysimilar ratings for most of the programs in a category, it is assumedthat the actual acceptance ranges for these customers for eachcharacteristic relevant to the category forms a narrow distribution,i.e., their values are close to each other. However, if in thedistribution of stated ratings, some outside values which are far awayfrom the majority are seen, then the indication is that these outsidevalues need to be adjusted.

There are many algorithms to find outsiders in a population. Forinstance, all the values may be sorted in the ascending order of theirabsolute distances from the mean, and gaps searched for at the lowerend. Those values that are located below the largest gap would beoutsiders.

For statistical validity, the mean and standard deviation of thepopulation less the outsiders may be calculated and a t significancetest conducted to determine if any of the outsiders belong to thepopulation. Only those that do not belong will be subject to adjustment.

D. Adjusting Content Profiles

As discussed above, in a rave review, the customer may state his/herdisagreement with the rating of a characteristic in a video program andput forward his/her own rating for each characteristic in the program.This provides a mechanism for adjustment of the content profiles.

In general, the present invention may use the ratings of experts or testgroups as the reference base. Generally, the calculation of theagreement scalar ac is based on the values of cp and sp. Since thevalues of cp and sp are used in calculating ac for all customers, anyinaccuracy in their values will affect the final results for allcustomers. (By contrast, an inaccuracy in the value of a customer's cvand sv only affects the results for that customer.) By definition,customers collectively make ratings relatively closer to reality thanany experts or test groups. In other words, the customer's rating isreality. For instance, if all the customers on the average tend tooverestimate a particular characteristic (for one or all programs), thenthe experts' or test groups' objective ratings for that characteristic(for one or all programs) should be raised to agree with the customers'perceptions.

Again, MSA may be used for the content profile adjustment, letting x beeither cp or sp and letting y^(n) be the value collectively suggested inperiod n by the customers for the variable, where y^(n) is defined as:y ^(n)=(1/I)Σ_(i) y ^(n) _(i),  Equation (7)where y^(n) _(i) is the value suggested by customer i during period n.By substituting y in Equation (5) into Equation (4), the x^(n+1) valuemay be calculated. For customers who do not state disagreement, theiry^(n) _(i) may be set to x^(n), i.e., the original content profile.Therefore, y^(n) is the average of the customers' suggested value atperiod n. The resultant x^(n+1) is the adjusted content profile afterperiod n.

This method would be less useful if only the content profiles of acharacteristic for individual programs may be adjusted. Often, therelative bias is systematic, i.e., as seen from the customer's side, thecharacteristic values may underestimate or overestimate the significanceof a characteristic for programs of certain or all categories. Thisproblem can also be addressed as follows.

For clarity in discussion, subscripts again will be used. y_(jk) is thecustomers' average suggested rating for characteristic k for program j.The distribution of the customers' ratings is assumed to be normal. Forsimplicity, time subscripts are dropped. A t value significance test isthen conducted as: $\begin{matrix}{{t_{jk} = {\left( {y_{jk} - {cp}_{jk}} \right)/{S_{{Yjk} - {CPjk}}\left( {{i = 1},2,\ldots\quad,{I;{j = 1}},2,\ldots\quad,{J;{k = 1}},2,\ldots\quad,K} \right)}}}{{where}\text{:}}} & {{Equation}\quad(8)} \\{S_{{Yjk} - {CPjk}} = \sqrt{{{{sv}^{2}/\left( {I - 1} \right)} + {{sp}^{2}/\left( {M - 1} \right)}},}} & {{Equation}\quad(9)}\end{matrix}$and where:t_(jk) is the t value for significance of difference between thecustomers' suggested rating of characteristic k for program j and thecorresponding assigned objective rating;s_(Yjk-CPjk) is the standard deviation between the distribution ofY_(jk) and that of CP_(jk);I is the total number of customers; andM is the total number of “experts.”

If t_(jk) is significant for a pre-defined level (say 0.05) with degreeof freedom of I+M−2, then cp_(jk) is determined to be significantlydifferent from y_(jk). In that case, an adjustment in cp_(jk) isnecessary, and MSA is calculated to obtain the new cp_(jk) from y_(jk).

With the above method, only the assigned objective rating of acharacteristic for individual programs is adjusted. In order to adjustthe assigned objective ratings of a characteristic for all the programsin a category, the following is used:T _(lk)=(1/J _(l))Σ_(j) _(l) t _(j-lk)  Equation (10)where:T_(lk) is the average of the t values for characteristic k in all theprograms for category l;t_(j-l k) is the t value for significance of the difference between thecustomers' suggested rating of characteristic k for program j_(l) andthe corresponding objective rating; andJ_(l) is the number of programs in category l.

If an adjustment of the assigned objective rating of a characteristicover several or all categories is desired, the t values of an even widerrange are averaged. For instance, if it is necessary to make anadjustment for all categories, calculate:T _(k)=(1/L*Σ _(l) J _(l))Σ_(l)Σ_(j-l) t _(j-lk),  Equation (11)where:T_(k) is the average of the t values for characteristic k in allprograms.

When a content profile value cp is changed to cp′, it is also necessaryto change the corresponding sp (deviation in cp) to sp′. Because thereis a distribution in cp, there must be some expert(s) whose rating(s) isbelow or above the mean (cp). If through the above calculation cpoverestimates/underestimates reality, it is assumed that only thoseexperts whose ratings are above/below the mean made anoverestimation/underestimation. Therefore, after the adjustment, the newdeviation sp′ should be smaller than sp.

One possible calculation of sp′ is:sp′=sp/(1+α*|cp′−cp|/cp)  Equation (12)Since cp>0, sp′<sp (i.e., sp always declines after adjustment). If α=1,cp=3, cp′=4, and sp=1, then sp′=0.75. The parameter α thus determinesthe rate of decreasing in sp with the rate of change in cp.

It should be pointed out that before making actual changes to contentprofiles determined by experts or test groups, it is desirable toconsult with the experts or test groups for the proposed changes. Thatwill not only preclude any unreasonable changes, but also will reducepossible future bias by the experts or test groups.

E. Customer Moods and Time Windows

Few people are purely single minded, especially when enjoyingentertainment. Besides a generic propensity, it is therefore reasonableto assume that each customer could have one or more viewing moods, andin each of the moods he/she would like to watch a particular set ofprogram categories. For normal and not highly capricious people, themoods should be time-specific, i.e., each mood has a time window, withinwhich the mood is effective.

On the other hand, people are not free all the time. The time when theycan enjoy entertainment is limited. The time window concept can be usedto represent this temporal limitation as well. Thus, each time windowcan be expressed as a pair of time variables, l and u, where l is thestarting point of the window and u is the ending point of the window.Customer profiles used in accordance with the preferred embodiment ofvideo scheduling preferably incorporate this concept of moods and timewindows.

In the present invention, each customer preferably has a generic moodand may also have some specific moods. Both generic moods and specificmoods may or may not be time-specific. In fact, a non time-specific moodcan also have a time window, only with l=u, i.e., its window covers thewhole day. Typically, for a particular customer, the time window forhis/her generic mood will have the greatest width, and the width of thewindows for his/her specific moods will decrease with an increase inspecification. In this sense, all the moods of a customer form a tree,in which the generic mood is the root, and a specific mood becomes thechild of another mood if the former's window is contained in thelatter's window. For example, if a customer has four moods: generic,peaceful, violent, speculative, the generic mood may cover all times,the violent mood may cover 6 a.m. to noon, the peaceful mood 6 p.m. tomidnight, and the speculative mood from 8 p.m. to midnight. Thus, thespeculative mood is a child (subset) of the peaceful mood. The mood atthe lowest (most specific) level of the hierarchy is generally used todevelop the program list for the customer (described below).

The definition of moods can be the responsibility of the customer. Whenballots are used to create the initial customer profiles, each ballotmay correspond to a mood. In other words, a mood may be equivalent to acustomer profile. The generic mood or generic customer profile isrequired unless there is an automatic system default mood or profile.Beyond that, the customer can fill out as many ballots as he/she likesto establish specific moods.

A satisfaction factor, sf, is attached to each mood. For the genericmood, sf=1, which is the base. sf increases as the time window narrowssince it is reasonable to believe that people get greater satisfactionas their more specific requirements are met. sf is either determined bythe customer or takes a default value. For instance, the system may seta maximum value on sf for the most specific window, which is two hourswide, and do a linear interpolation to find the sf values for windows ofgreater widths. If the customer provides the of values for his variousmood windows, the values will be normalized in light of the base valueof one and the above-mentioned system-set maximum value.

With the introduction of time windows, each customer i (or customer-moodi, to be more accurate) will take on time window superscripts as i^(l)^(—) ^(i, u) ^(—) ^(i), while each program j will become j^(l) ^(—)^(j, u) ^(—) ^(j), where l_(j) is the starting time of program j andu_(j) is the ending time of program j. The calculation of the agreementscalar ac then proceeds as will be described in the next section.However, the calculation of as, the final objective value, becomes:as _(ij) =sf _(i) *[wc _(i) *ac _(ij) −wf*f(l _(i) ,u _(i) ,l _(j) ,u_(j))],  Equation (13)where f(l_(i), u_(i), l_(j), u_(j)) gives a punishment value expressingthe customer's dissatisfaction due to the mismatch between the timewindow of customer-mood i and the broadcast time of program j, sf_(i) isthe normalized satisfaction factor of customer-mood i, wc_(i) is theweight for the existing agreement scalar, and wf is the weight for f,which needs to be determined through practice.

The major issue here is the form of the punishment function f.Intuitively, f=0 when the mood window contains the broadcast window,i.e., l_(i)≦l_(j), and u_(i)≧u_(j), and f increases as the two windowsmove away from each other. Since u_(i)−l_(i)≧u_(j)−l_(j), i.e., the moodwindow is not narrower than the broadcast window, the time discrepancy dbetween the two windows may be expressed as:d=max(0,l _(i) −l _(j) ,u _(j) −u _(i)),  Equation (14)So f=f(d).

It is reasonable to expect that the customer's dissatisfaction increasesrather sharply when the mismatch of the time windows first begins, whichmeans he/she will miss some part of the program. But when the timemismatch increases further, the customer's discontent will level off.For example, the customer will feel quite upset if he/she misses thebeginning ten minutes of a program which he/she likes. However, ifhe/she has already missed the first one hour and a half, his/herdissatisfaction will not increase much if he/she misses the remaininghalf an hour. This non-linear relationship can be well expressed by thefollowing negative exponential equation:f(d)=α(1−e ^(−βd)),  Equation (15)where α is the maximum dissatisfaction that a customer could have formissing a program, and β is a parameter which determines how sharplyf(d) increases with d. The greater the value of β, the steeper the curvewould be. It can be seen through Equation (is) that f(d)=0 when d=0, andf(d)=α when d=∞. Thus, the punishment function becomes:f(l _(i) ,u _(i) ,l _(j) ,u _(j))=α(1−exp(max(0,l _(i) −l _(j) ,u _(j)−u _(i)))).  Equation (16)Given the form of Equation (13), α may be set to one since the extent ofdissatisfaction can be adjusted by the weight parameter wf.III. Calculation of Agreement Matrix

The calculated agreement scalars, ac, form an agreement matrix, AC,which provides measurements of the similarity between the customerprofiles and the content profiles. Its calculation incorporates thedesired amounts of the various characteristics used to define theprograms, their importance (weights) to each customer, and the amountsof these characteristics present in each program as determined byexperts or test groups. Assuming there are I customers, J programs, Kcharacteristics, and M experts, then each cell in the initial agreementmatrix (agreement scalar for cv_(ik) and cp_(jk)) may be calculated as:ac _(ij)=1/[1+(1/K)Σ_(k)(wv _(ik) /W _(i))t _(ijk)] (i=1, 2, . . . I,j=1, 2, . . . J),  Equation (17)where: $\begin{matrix}{\begin{matrix}{{t_{ijk} = {{\left( {{cv}_{ik} - {cp}_{jk}} \right)}/s_{{CVik} - {CPjk}}}},} & {{{if}\quad{cv}_{ik}}>=0} \\{{= \infty},} & {{{if}\quad{cv}_{ik}} = {- 1}}\end{matrix}\left( {{i = 1},2,\ldots\quad,{I;{j = 1}},2,\ldots\quad,{J;{k = 1}},2,\ldots\quad,K} \right)} & {{Equation}\quad(18)}\end{matrix}$and: $\begin{matrix}{s_{{CV}_{ik} - {CP}_{jk}} = \sqrt{{{sv}_{ik}^{2}/(N)} + {{sp}_{jk}^{2}/\left( {M - 1} \right)}}} & {{Equation}\quad(19)}\end{matrix}$and:

-   ac_(ij): agreement scalar between the profiles of customer i and    that of program j;-   t_(ijk): t value for significance of difference between the rating    of characteristic k in customer i and that in program j;-   s_(CVik-CPjk): standard deviation between the distribution of    cv_(ik) and that of cp_(jk);-   W_(i): Σ_(k) wv_(ik), i.e., the sum of all weights for customer i;-   M: number of “experts” who rate program j; and-   N: number of times of consideration before the customer reaches a    final decision on the rating for cv.

The magnitude of each of the t values shows the deviation of thecustomer's ratings of a characteristic from that of the video programgiven the distributions of the ratings by both the customer and theexperts or test group. If the t value is significant at a predeterminedlevel of significance, e.g., 0.05 for degree of freedom of 2M−2, thenthe two distributions could be regarded as belonging to the samepopulation. The average of all the t values can serve as an indicator ofthe divergence (distance in characteristic space) between the profile ofthe customer and that of the program. Therefore, the variable ac, whichis basically the reciprocal of the average of the t values (reciprocalof the distance), exhibits the level of agreement between the twoprofiles. Thus, acε(0,1) and reaches its maximum value of 1 (perfectagreement) only when Σ_(k)wv_(ik)*t_(ijk)=0 or wv_(ik)*t_(ijk)=0 (i=1,2, . . . , I; j=1, 2, . . . , J; k=1, 2, . . . , K) since wv_(ik)>=0 andt_(ijk)>=0. In other words, perfect agreement will be met only whenthere is no difference between the customer profile and the contentprofile, or when there are differences only on certain characteristicsand the customer ignores those characteristics. As a result, sorting allthe programs in the ascending order of ac renders a recommendation listof programs for the customer.

In the original formulation of the t significance test, both M and N arethe sample sizes. While M is the number of experts or members in thetest group, N represents the number of times of consideration before thecustomer reaches his/her final decision on the rating for cv. N's valuemay be determined empirically through experiments. Generally speaking,the higher N's value, the lower the flexibility in the customer'sacceptance for various characteristics on average. Preferably, N=M−1 sothat dispersions in cv, which is interpreted as the customer'sacceptance range, and in cp, which represents the difference in theexperts' voting, are equally counted. This calculation is underpinned bythe assumption that both the distributions of the experts' vote and thecustomer's rating are normal (Gaussian). Although the assumption is notguaranteed, that is the best that can be hoped for.

Once the initial customer profiles and initial content profiles havebeen established, a simpler form of Equation (17) may be used bycombining wv and s into a single measure of importance: $\begin{matrix}{{{w_{ik} = {{{wv}_{ik}/\left( {l/J} \right)}\Sigma_{j}s_{ijk}}},{{where}\text{:}}}{{s_{ijk} = \sqrt{{{sv}_{ik}^{2}/M} + {{sp}_{jk}^{2}/M}}},}} & {{Equation}\quad(20)}\end{matrix}$where sv_(ik) is the spread in customer i's rating for characteristic k(inversely correlated with the importance of k to i), and sp_(jk) is thespread in the experts' ratings for characteristic k. Thus:ac _(ij) l/[l+Σ _(k) w _(ik) |cv _(ik) −cp _(jk)|],  Equation (21)This simpler notation is preferred and will be used throughout thediscussion below. However, the algorithms described below are all easilyextended to the more complex model of Equation (17). Generally, the morecomplex form of the agreement matrix (Equation (17)) is only preferredwhen the customers are asked questions to build their customer profiles.The simpler form of the agreement matrix (Equation (21)) is preferredwhen the profiles are initialized using demographics and updated usingpassive monitoring, as in the presently preferred implementation of thepresent invention.

For purposes of illustration, a calculation of a simple agreement matrixwill be described here.

It is assumed that there are only two customers:

(1) John and (2) Mary. Their sample customer profiles are as follows:romance high-tech violence characteristic (cv): 1 John 3.0 9.0 7.0 2Mary 9.0 3.0 0.0 standard deviation (sv): 1 John 1.0 2.0 1.0 2 Mary 1.00.5 0.0 weight (wv): 1 John 2.0 9.0 5.0 2 Mary 8.0 3.0 7.0

The available programs are as follows: Program Titles 1 Star Trek 2Damnation Alley 3 Forever Young 4 Terminator II 5 Aliens 6 FatalAttraction

The sample content profiles are as follows: program romance high-techviolence char. (cp): 1 Star Trek 2.0 9.0 4.0 2 Damnation Alley 5.0 0.01.0 3 Forever Young 8.0 3.0 0.0 4 Terminator II 0.0 10.0 8.0 5 Aliens0.0 8.0 9.0 6 Fatal Attraction 7.0 0.0 8.0 standard deviation (sp): 1Star Trek 0.5 1.0 1.0 2 Damnation Alley 1.0 0.0 1.0 3 Forever Young 1.00.5 0.0 4 Terminator II 0.0 1.0 1.5 5 Aliens 0.0 1.0 1.0 6 FatalAttraction 2.0 0.0 1.0

After normalizing w using: $\begin{matrix}{W_{ik} = {{WV}_{ik}/\left( {{\frac{\Sigma}{j}{Scv}_{ik}} - {{cp}_{jk}/J}} \right)}} & {{Equation}\quad(22)}\end{matrix}$

where s_(CJik-Cpjk) is defined in Equation (19), the above input dataproduces the following weight matrix (w): characteristic: customerromance high-tech violence 1 John .166 .425 .409 2 Mary .292 .192 .516

Given the weight matrix and the characteristic profiles of the customersand the programs, the agreement matrix may be calculated. For example,the agreement scalar between customer 1 and program 2 is:$\begin{matrix}{{a\quad c_{12}} = {1/\left( {1 + {w_{11}{{{cv}_{11} - {cp}_{21}}}} + {w_{12}{{{cv}_{12} - {cp}_{22}}}} + {w_{13}{{{cv}_{13} - {cp}_{23}}}}} \right)}} \\{= {1/\left( {1 + {{.166}*{{3 - 5}}} + {{.425}*{{9 - 0}}} + {{.409}*{{7 - 1}}}} \right)}} \\{= {.131}}\end{matrix}$

The final agreement matrix (AC) is thus: Program Customer 1 2 3 4 5 6 1John .418 .131 .138 .429 .365 .170 2 Mary .160 .307 .774 .110 .108 .159

From the agreement matrix, it is evident that John prefers “Star Trek”,“Terminator II”, and “Aliens”, while Mary prefers “Forever Young” and“Damnation Alley”. This is the results that would have been expectedfrom the profiles, only here the preferences have been quantified.

Of course, in the simple case where merely the presence or absence ofparticular characteristics are measured, the agreement matrix would lookfor identity in the most categories rather than the distance between thecustomer profile vector and the content profile vector using thetechniques described above.

Iv. Scheduling Video Delivery in Accordance with Customer and ContentProfiles

The introduction of time dimensions makes possible the scheduling ofvideo programs, i.e., the assignment of programs to days and to timeslots in accordance with each customer profile.

A. Scheduling Constraints

Solving the problem of assigning days and time slots simultaneously isoften impractical because of the exponential increasing order in thenumber of possible combinations. Therefore, the two tasks are preferablyperformed separately through heuristic methods.

The first task, assigning programs to days, is simplified significantlyby the fact that in the present system the customers' preferences aredifferentiated mostly by time slots (hours), rather than by days. Whenthe customer defines weekday mood time windows, it is assumed that thewindow will apply to any weekday. Of course, the customer may definesome weekend time windows, which apply to either Saturday or Sunday.Therefore, for a given set of programs available for a week, the majorquestion is not on which day to broadcast them, but during which hoursto broadcast them.

A possible approach to scheduling is that for each program its top nmost-preferred broadcast windows are determined from the average of theobjective values as calculated using Equation (13). The scheduler thenuses some methods to find a solution in which the average objectivevalue reaches a reasonably high value, and in which the time slots arecovered. There are many such methods available, such as integer linearprogramming.

An extra complexity is the possible necessity of repeating some programsduring a day or a week, because of their high popularity. A simpleapproach is to let the scheduler first determine the number of necessaryrepetitions, and then add the number in as constraints in theprogramming. However, it should be noted that the above approach is forjust one channel. If there are multiple channels, then it is usuallynecessary to first categorize the channels, find their respective“target audience”, and then run the scheduling procedure on the targetaudience of each channel.

Attention should also be paid to the mutual exclusion among theoverlapping time windows of a customer. Although the customer may definetime windows which conflict with each other, in terms of overlapping andcontainment, only one of the windows in the conflicting set can be usedin the final assignment. This condition should be added to theconstraints.

B. Scheduling Algorithm

With the above scheduling constraints in mind, the present inventorshave developed an algorithm which uses customer profiles and contentprofiles for scheduling the broadcast of movies and other shows over avideo distribution network which allows the simultaneous distribution ofmany channels from a head end to the set top multimedia terminalsassociated with many customers' television sets. The same approach isthen used to develop “virtual channels” at the set top multimediaterminals based on domain or genre or tastes of individuals so that thecustomer can view the video programming predicted to be most desirableto that customer. The “virtual channels” may be displayed on dedicatedchannels, or the recommended programming may be highlighted directly onthe electronic program guide or displayed on the customer's screen asrecommended programming selections. Also, the channels may bereprioritized for presentation on the electronic program guide on thebasis of the calculated “virtual channels.” Similarly, video programmingof a particular type, even if not part of the “virtual channels” may behighlighted on the electronic program guide as desired. The algorithmfor determining the recommended programming is based on theabove-mentioned “agreement matrix” which characterizes theattractiveness of each movie and video program to each prospectivecustomer. In short, a broadcast schedule and/or virtual channel isgenerated which is designed to produce the greatest total customersatisfaction. The generation of the agreement matrix and the schedulingof programs in accordance with the generated agreement matrix will nowbe described in more detail.

As described above, the agreement matrix may be produced by comparingthe customer profiles and the content profiles. In the followingdescription, it is assumed that the agreement matrix is normalized sothat all agreements between customers and movies lie between zero andone.

The basic problem of scheduling a cable television broadcast can beformulated as follows.

Given an agreement matrix A where ac_(ij) is the agreement scalarbetween customer i and program j, find: $\begin{matrix}{\max\quad\underset{J \in N_{i}}{\sum\limits_{i}\sum\limits_{j}}a\quad c_{ij}} & {{Equation}\quad(23)}\end{matrix}$where J is a set of programs to be broadcast drawn from a set Q ofcandidate programs available for broadcast, the first summation is overall customers i, and the second summation (of j) is over the n_(i)programs in the set of programs K_(i) that customer i would most desireto watch. In other words, given the agreement matrix between customersand programs, it is desired to pick the set of programs which maximizesthe agreement between customers and those programs which the customersmight watch. For example, if a hundred programs are being broadcast anda given customer would not consider more than five of them, it does notmatter how much the customer likes the other ninety-five programs.However, as noted above, the actual problem can be much more complex,since different agreement matrices can depend on the time of day andsince multiple time slots cannot be scheduled independently.

If only one program were to be broadcast using the method of theinvention, the above optimization problem is trivially solved by summingeach column of A (calculating Σ_(i) ac_(ij)) and picking the program jwhich gives the largest value. When many programs are being selected,however, it is not possible to try all possible combinations; therefore,heuristic methods must be used.

The following algorithm is an example of a greedy algorithm whichprovides an efficient algorithm for approximately solving the abovescheduling problem including the fact that it is desired to select n_(i)programs for each customer i (the “viewing appetite”). In other words,n_(i) represents the number of programs scheduled for broadcast to aparticular customer at any time. In a preferred is embodiment, n_(i)corresponds to the number of “virtual” channels available to eachcustomer.

In accordance with the invention, the “greedy” scheduling algorithm willfunction as follows. As illustrated in FIG. 2, at each step of thealgorithm:

1) Pick the program j which yields the greatest satisfaction summed overthe current customer population (i.e., those eligible to receive programj).

2) Decrement the viewing appetite n_(i) of those customers who have thegreatest agreement scalars with the currently selected program.

3) Remove from the customer population any customers whose viewingappetite has dropped to zero (n_(i)=0), since they have all the showsthey need and hence are not a factor in selecting further shows.

The scheduling process stops when the number of programs selected, m,equals the number of broadcast channels available, M, for the scheduletime.

A more precise description of the process may be described in pseudocodeas:

0) Initialize:

-   -   Let ap_(i)=n_(i) for all i set all customers' appetites to n_(i)    -   Let V={1} initialize the customer population to include all        customers    -   Let m=0. start with no programs selected        As described below, different initializations are possible to        account for programs which will always be broadcast. Also, if        individual appetites are not available, all can be set to a        single value, n.        1) Select the currently most popular program:    -   Select program j which gives max Σ_(i in V) ac_(ij)    -   Let m=m+1 increment number of programs selected    -   If m=M, stop, stop if done    -   otherwise proceed to (2).        2) Decrement the appetite of those customers who like the        currently selected program:

Select the customers i in V for which ac_(ij) is above a threshold valueα. Then decrement the appetites for selected customers by lettingap_(i)=ap_(i)−1.

3) Remove customers from the current customer population V who have noappetite left:

-   -   Remove from V customers j for whom ap_(ij)=0.    -   Go to (1).        This method automatically produces a schedule in which a variety        of programs are selected according to the spread of customer        interests. Note that the simpler algorithm of selecting the most        popular programs (those with highest agreement matrices) will        not produce acceptable results, for if a majority of customers        prefer action films, then only action films would be selected,        leaving the minority of customers with no films that they find        attractive.

FIGS. 1-3 summarize the above-mentioned procedures for establishingcustomized channels of preferred programs in accordance with theinvention.

As illustrated in FIG. 1, a schedule of available shows and theircharacteristics (content profiles) is created and stored in a databaseat step 102. As noted above, the characteristics of the shows may bedetermined by “experts” or test groups by completing questionnaires andthe like, or the content profiles may be generated from the frequency ofusage of certain words in the text of the video programs (the on-linedescriptions or the script). Alternatively, the content profiles may bedetermined by combining the customer profiles of those who “liked” thevideo program during a “rave review.” Preferably, the content profilesare downloaded all at once for a given time period along with thecorresponding scheduling data as part of the electronic program guidedata and sent via a separate data channel. On the other hand, thecontent profiles may be transmitted as part of the bit stream of thevideo program (for digital transmission), in the vertical blankingintervals of the video program (for analog transmission), or by otherappropriate means.

At step 104, the customers' preferred characteristics (customerprofiles) are created and stored in a database. As noted above, thecustomer profiles represent the customers' preferences for the programcharacteristics and preferably differ in accordance with the time of dayto account for different moods of the customer and different customerswithin each household. In a preferred embodiment, the customer profilesfor each household are stored in the set top multimedia terminal forthat customer's household.

The content profiles received with the electronic program guide data arepreferably stored at the set top multimedia terminal and compared by theset top multimedia terminal to the customer profiles for each customer.An agreement matrix is then created at step 106 using the techniquesdescribed above. Once the agreement matrix has been generated, thoseprograms with the highest values for ac, i.e., the closest distance(1/ac) and hence closest match to the customer's profile or profiles,are prioritized and selected for presentation as “virtual channels” (inthe case of creating “virtual channels” at a set top multimediaterminal) or as the programming channels (in the case of schedulingvideo programming at the CATV head end) at step 108. This process isdescribed in more detail herein with respect to FIG. 2.

In a simple embodiment of the invention in which no feedback is used toupdate the customer profiles, no further activity is necessary. However,it is preferred that the customer and/or content profiles be updated toallow for changes in the customers' preferences as well as to correcterrors in the original determinations of the profiles. Accordingly, atstep 110, the customers' set top multimedia terminals maintain a recordof the video programs that are actually watched by the customer for aperiod of time (say, 10 minutes) sufficient to establish that thecustomer “liked” that program. Of course, the monitoring function may beselectively activated so that the profiles are not always updated, aswhen a guest or child takes control of the television at an unexpectedtime.

Finally, at step 112, the customers' profiles are updated to reflect theprograms actually watched by the customers. Such updating techniques aredescribed above and further below with respect to FIG. 3.

FIG. 2 illustrates a technique for selecting video programs for “virtualchannels” at the customers' set top multimedia terminals or,alternatively, for scheduling video programming at the head end from theavailable video programming sources. As illustrated, the method isinitialized at step 202 by determining which customer profile orprofiles are active for the time period to be scheduled, by determiningthe customers' appetites (number of channels available fortransmission), and by determining the database of video programming fromwhich the schedule may be created. For example, at the head end, thevideo programming database may be any video programming available fortransmission during the designated time frame, while at the set topmultimedia terminal, the video programming database comprises only thevideo programming on those channels which the customer is authorized toreceive.

Once the agreement matrix for the available video programs has beendetermined, at step 204 the most popular programs for a single customer(at the set top multimedia terminal) or a cluster of customers (at thehead end) are selected and removed from the list of available programsduring the relevant time interval. Of course, in the case of schedulingat the set top multimedia terminal, the video programs scheduled onto“virtual channels” are still received on their regular channels and the“virtual channels” are assigned to unused channels of the set topmultimedia terminal. At step 206, it is then determined whether thecustomer's appetite is satisfied (at the set top multimedia terminal)and whether all the customers' appetites are satisfied (at the headend). If all relevant appetites are satisfied, the scheduling algorithmis exited at step 208. On the other hand, if all customer appetites arenot satisfied, the appetites of the customers likely to watch theselected program are decremented at step 210. Hence, only thosecustomers with preferences which relatively “match” the characteristicsof a particular video program have their appetites decremented. At step212, those with no appetites (channels for scheduling) left are removedfrom the scheduling list. The process is then repeated starting at step204 for those with channels left to schedule.

When establishing “virtual channels” in accordance with the invention,it is important to know which customer profile or profiles to use increating the agreement matrix. In a preferred embodiment, this isaccomplished by using the customer profile or combination of customerprofiles which are given priority during a particular time interval fora predicted customer mood. This determination is made independent of theperson actually viewing the television. However, the system of theinvention may be easily modified to permit the customer to identifyhimself or herself by providing a user ID to the set top multimediaterminal so that a particular profile of that customer may be selectedin the determination of the agreement matrix. In other words, customernames may be matched to particular profiles based on selections madewhen that customer's user ID has control of the television. In addition,combined profiles may be created which best reflect the combined viewingtastes of several persons in the same household. On the other hand, thesystem may come with preselected profiles which the customer may selectto use as his or her initial profile. After a certain amount of time,the system would recognize a particular profile as belonging to aparticular viewer or combination of viewers so that it would eventuallybe unnecessary for the customers to input their user IDs. In otherwords, the system would “guess” which customers are viewing by notingwhich customer profile is closest to the shows being selected. Ofcourse, this latter approach requires the customer profiles to bematched to individuals rather than just time slots as in the preferredembodiment.

FIG. 3 illustrates a preferred technique for updating customer profilesin accordance with the invention. As illustrated, the initial customerprofiles are selected at step 302 using any of the techniques describedabove. At step 304, the agreement matrix is calculated to determinewhich video programs the customer might desire to view in the selectedtime period. Then, at step 306, the passive monitoring feature of theinvention is invoked to determine if the customer actually watched thevideo program selected from by the agreement matrix. If the customerwatched the predicted program, then the customer profile is presumedaccurate at step 308 and no adjustment is made. Of course, the customerprofile may be positively reinforced by varying the adjustmentincrement. However, if the customer did not watch the predicted videoprogram, the customer profile for the appropriate time interval isselected at step 310 which has characteristics closest to those of thevideo program actually watched. That customer profile is then adjustedusing the techniques described above. The adjusted profile is thenconsidered valid until the next time slot is encountered at step 312.The agreement matrix is then recalculated at step 304 for the newcustomer profiles and video programs offered in the next time slot.

Particular hardware implementations of the invention in a set topmultimedia terminal and/or a video head end will be described in SectionVI below.

C. Scheduling Variations

Many variations to the above generalized scheduling scheme are possiblewithin the scope of the invention. The following variations may be usedby those skilled in the art, but, of course, this list is notcomprehensive.

A. Special programs such as standard network broadcasts may be includedin the scheduling. In this embodiment, when certain programs havealready been scheduled for broadcast, such as standard network programsor specially selected popular movies, the above algorithm is modified toaccount for the effect of these programs on customer interest in theremaining video programming. This is easily done by initially runningthe above algorithm with step (2) modified to simply include theprescheduled programs rather than selecting new ones. When allprescheduled programs have been “scheduled” (i.e., customers likely towatch the prescheduled programs have been removed from the customer pooland the broadcast slots have been filled), then the scheduling algorithmproceeds as usual. As desired, this will lead to additional movies beingselected which will appeal most to customers who will probably not bewatching the standard network broadcasts.

B. The effect of recent broadcasts may be included in the scheduling.The above scheduling algorithm is presented for a single time slot. Inactuality, the video programs selected must depend on which other videoprograms have been shown recently. This can be done in several ways. Forexample, one can remove recently shown movies from the list of moviesavailable to broadcast. Alternatively, one can remove recently shownmovies from the list of movies available to broadcast when theirpopularity (number of customers per broadcast) drops below a threshold.This approach is better in that it allows new hits to be broadcastmultiple times. More complex models may explicitly include a saturationeffect by shifting the agreement matrix based on the number of similarvideo programs recently viewed.

C. The effect of overlapping time slots, such as so-called “near videoon demand” may be included in the scheduling. For this purpose, theabove algorithm can be modified to account for the fact that popularvideo programs may occupy more than one time slot and that time slotsmay overlap. As an extreme example, it may be desirable to offermultiple overlapping broadcasts of a popular movie on vacant channels,e.g., at 15 minute intervals. In this example, the customer appetiteconcept set forth above would be augmented by a more sophisticated modelwhich includes the fact that customers turn on the television atdifferent, possibly random times, and only want to watch shows which arestarting at times close to the time they turn on the television.

D. The effect of moods may be included in the scheduling. If one hasdifferent agreement matrices for different customer moods (eitherreflecting multiple customers with different tastes using the sametelevision or reflecting one customer having different viewingpreferences due to mood), the above algorithm can be trivially extended.The different moods are just treated as different customers, with theappetite for each mood selected to be proportional to how often thatmood occurs in the time slot being scheduled. This will result inprograms being scheduled for each of the potential moods; the customercan then pick his or her preferred show.

E. Programs may be matched to channels for scheduling content basedchannels. Customers may prefer to have channels which have a consistentcontent or style (e.g., sports or “happy” shows). The basic algorithmpresented above could be modified so that some slots are reserved sothat shows of a given type (e.g., close to a given set ofcharacteristics typical of a channel) can be selected if they have notalready been chosen in the main scheduling algorithm.

F. How the customer appetites are decremented may be selected forscheduling purposes. In the above scheduling algorithm, whenever aprogram is chosen, the viewing appetite of all its “audience” isdecremented, and those who have used up their appetites are removed fromthe viewing population. Since the checking of viewing appetite is madeonly of the audience of the currently chosen program, the outcome of thescheduling process depends in part on the definition of audience—whowill potentially watch the program. Above it was assumed, forsimplicity, that customers should be included in the potential audienceif their agreement with the program was above a threshold. However, manyvariations on this are possible. For example, one could pick a fixedaudience size for each show. For example:

Select the n_(w) customers i in V for which ac_(ij) is maximum. n_(w)can be calculated, for example, as n*I/M as the average customerappetite, n, times the number of customers, I, divided by the number ofprograms, M, to be broadcast. One could also make the threshold avariable, either by decreasing the threshold or the number of customersn_(w) over the course of developing a schedule so that the firstprograms scheduled would have large audiences, while the last programsscheduled (those done after most customers needs are satisfied) wouldhave smaller audiences. Ideally, this would be done in accordance withobserved audience size distributions.

G. The lead-in effect may be included in the scheduling. The algorithmcould be modified to account for higher viewing levels in shows whichfollow immediately after popular shows (the “lead-in” effect). Asdescribed above, this can be accounted for by treating errors inpredicting customer behavior differently if they result from thecustomer remaining tuned to a channel.

H. The effect of repetitive showings may be included in the scheduling.Since viewership is a strong function of the time of day and of the dayof the week, one cannot assess the popularity of a show based on thenumber of people watching it without controlling for the time slot.Similarly, movies shown at the same time as very popular programs or asvery similar programs tend to have fewer viewers—the audience will bedivided. The algorithm given above does not rely on absolute viewershipnumbers and so does not have these problems.

Similarly, when many similar programs are shown over a short span oftime, there is viewer “burnout”. In other words, if the same movie isshown repeatedly over the course of a month, it will get fewer viewerson later showings. As another example, if many golf programs arebroadcast during a week, each customer's desire to watch golf willsaturate, and viewership will decay. Predictions of what a customer willwant to watch only makes sense if they have not watched the same (oralmost identical) show recently. Thus, changes to the customer profilesand content profiles should not be made if, for example, a customer doesnot select a movie to watch which they recently watched. However,depending on the indexing scheme used to store viewing habits, checkingto see if a similar program was recently watched, while straightforward, may require a significant amount of database search.

I. Customer profiles can be modified on an individual basis. Sincedifferent people often watch the same television, and most feedbackdevices in popular use do not recognize which customers are present,customer preferences cannot be characterized by a single agreementmatrix. Also, customers may have different agreement matrices dependingon their mood. If more than one agreement matrix per television isinitially estimated (e.g., by interviewing multiple customers), then theabove algorithm can be modified to only count a prediction as wrong ifnone of the agreement matrices for a given television yields predictionsthat agree with what was actually watched. One or more of the agreementmatrices for the television could then be updated using the algorithm.This is not ideal, in that one does not know which mood (or customer)was present, but the best that one can do is assume that it was the mood(customer) whose agreement matrix came the closest to giving the correctprediction. On the other hand, the customer may simply identify himselfor herself when the television is turned on, and preferably, may specifywhich profile to use based on who is present and/or the customer's mood.

All of these effects can be taken into account in developing customerprofiles and content profiles and in scheduling video programs inaccordance with the invention.

V. Passive Characterization of Customers Using Feedback

Thus far, the invention has been described in the context of a“filtering” system in which all of the video programming available atthe head end is scheduled on “customized” channels in accordance withthe customer profiles of customers and in which a subset of theprogramming on the “customized” channels available to each customer isselected using an agreement matrix for presentation to the customer as“virtual channels” tailored to that customer's characteristic profiles.However, one of the more interesting applications of the above-mentionedcustomer profile system is that the same customer profiling system maybe used to provide feedback from individual customers regarding whatcharacteristics they find most desirable in the broadcast shows. Byobtaining this information, the customer profiles may be appropriatelyupdated as described above. As will now be described, the videoprogramming schedules also may be updated to reflect the customers'actual preferences, and information may be combined with the customerdemographics and customer profiles to provide targeted advertising andtargeted shop at home opportunities for the customer.

A key feature of many video/cable television installations is that it ispossible to obtain active feedback from the customer: either simply whatwas watched at each time or, more completely, how much the customers (intheir estimation) liked what they saw. Monitoring viewing patterns isreferred to herein as “passive” feedback, since unlike such prior art“active” feedback systems where the customers actually rate how muchthey like particular programs (see, e.g., Strubbe, U.S. Pat. No.5,223,924), passive monitoring in accordance with the invention does notrequire any customer actions. As will be described below, passivefeedback can be used to improve characterizations of customers'preferences for programs, which, in turn, leads to better selection andscheduling of programs. Also, as just noted, passive feedback providesnew target marketing opportunities.

Profiles of customers indicating which video program characteristicsthey prefer can be combined with content profiles of video programsindicating which characteristics they possess to give a measure of howwell each customer should like each video program. One way of doing thisis to construct an agreement matrix as described above. Passive feedbackis used in conjunction with the agreement matrices to improve customerprofiles and content profiles and hence to improve program schedules.

Passive feedback can be used both to improve individual customerprofiles and to improve customer profiles for clusters of customers.Customer profiles of customer clusters then can be used to improve theprofiles of all customers constituting the cluster. As a simple example,if one finds that most people in a cluster like movies by a particulardirector, then one could conclude that the other customers in thecluster would probably like that director as well.

As with methods for updating individual customer profiles, one cancharacterize clustering methods for grouping together customers (e.g.for a “video club”) as lacking feedback, using passive feedback, orusing active feedback. As described above, the basic agreement matrixmethod of the present invention, in which video programs arecharacterized by certain identifying characteristics which are thencompared to the customers' preferences for those characteristics, usesno measurement of what is watched or other feedback. However, as alsonoted above, that method can be supplemented with customer ratings ofmovies (active feedback) or passive feedback of who watched what movieswhen so that the customer profiles and/or content profiles may beadjusted. It is now proposed that those monitoring techniques beaugmented by a clustering algorithm which combines passive feedback withthe use of customer profiles and content profiles. This offers theadvantage of using the technique of the invention even when no initialcustomer profiles are available and there is no past history of what thecustomers have watched.

The technique starts, optionally, with a profile of the customer interms of what movie characteristics he or she finds important. It thenrefines the importance given to different characteristics based on howaccurately the characteristics predict what movies the customer actuallywatched.

A. Algorithm for Passive Updating of Customer Profiles

The same notation as used above will be used here to describe themethods for using passive feedback to improve profiles of customers,movies, and customer clusters. Namely: cv_(ik) is the amount ofcharacteristic k that customer i desires, wv_(ik) is the importance ofcharacteristic k to customer i, and cp_(jk) is the degree to which moviej has characteristic k. For notational simplicity, it is assumed belowthat the weightings are normalized (Σ_(i) wv_(ik)=1) so that thecustomer weightings add to one. There are: J movies, K characteristics,I customers, M “experts”, and P movies to be selected for a givenviewing interval (e.g., a day or week). The agreement matrixincorporates both the desired amount of each characteristic and itsimportance to the customer using Equation (21) set forth above, exceptthat w_(ik) has been normalized.

Given a set of J movies available with characteristics cp_(ik), and aset of customer preferences, cv_(ik), customer i would be predicted topick a set of P movies to maximize:$\sum\limits_{\underset{{best}\quad P\quad{of}\quad J}{j\quad i\quad n\quad{the}}}{a\quad c_{ij}}$

If the customer picks a different set of P movies than was predicted, cvand w_(ik) should be adjusted to more accurately predict the movies heor she watched. In particular, cv and w_(ik) should be shifted to reducethe match on movies that were predicted to be watched but were notwatched, and to increase the match on movies that were predicted not tobe watched but were watched. There are several ways to do this. One isto shift cv for each wrong prediction for customer i and movie j using:cv_(ik) =cv _(ik)−Δ(cv _(ik) −cp _(jk)).  Equation (24)

This will increase the match by making cv closer to cp if Δ ispositive—and is representative of the case where the algorithm failed topredict a movie that the customer watched. The size of a determines howmany example movies one must see to replace what was originallybelieved. If Δ is too large, the algorithm will be unstable, but forsufficiently small Δ, cv will be driven to its correct value. One couldin theory also make use of the fact that the above algorithm willdecrease the match if Δ is negative, as for the case where the algorithmpredicted a movie that the customer did not watch. However, there is noguarantee that cv will be moved in the correct direction in that case.

One can also shift w_(ik) using a similar algorithm:

Rather than making changes simply based on “watch/did not watch”, onecan weigh the changes (i.e., alter the size of the Δ's) based on thedegree of customer like/dislike of the program. In addition, one canalso modify the algorithm to take into account other known determinantsof customer behavior. For example, customers tend to continue watchingprograms which are shown on the channel that they are currentlywatching. This means that even if the agreement matrix correctlypredicts that a customer would prefer a program on a different channel,the customer may not discover the other program (or bother to change toit). In this case, the agreement matrix was not incorrect and so thecustomer and content profiles should not be altered. This can easily beincorporated into the passive feedback algorithm by using smallerchanges (smaller Δ's) when a customer remains tuned to the same channel(possibly virtual channel) than when they simply switch channels.

C. Customer Clustering

As noted above, customer profiles can be kept for groups of customers aswell as for individual customers. Grouping customers together intocustomer clusters offers several advantages. Most importantly, if theclusters are accurate, improvement of customer profiles will be muchfaster, since far more movies are viewed per week by a cluster than byany individual in the cluster. Clustering also provides a means ofsetting up an initial profile for new individuals joining a videoservice in accordance with the invention, as they can, as a startingpoint, be given a profile based on demographic data or on surveys theyfill out.

There is a long tradition of clustering people based on demographic orother data, and many clustering algorithms exist ranging fromtraditional methods such as factor analysis or the k-means clusteringalgorithm to more esoteric neural network-based methods such as Kohenennetworks. Any of these can be used for the task described here, but thepresent inventors prefer the k-median clustering algorithm. Clusters canbe formed based on (1) what programs people watch, (2) what features ofprograms customers rate as important (e.g., how similar their agreementmatrices are), or (3) a combination of programs and features. One canalso include demographic or psychographic customer profiles or otherinformation.

The clustering mechanism selected must address several technical issues.Most importantly, the clustering algorithm must take into account thefact that different attributes used for clustering may have differentdegrees of importance, and may be correlated. If one uses as aclustering criterion a pure measure, such as maximizing the number ofprograms watched in common, or maximizing the degree of similarity ofthe customers' agreement matrices, this is not a problem, but if theseattributes are combined with other information such as demographics, thealgorithm must determine an appropriate metric, i.e., combinationweights for the different measures.

Once clusters have been determined, they can be used in several ways. Asthe profiles for the clusters are updated based on what the customers inthe cluster watched, the profiles for the individuals in the cluster canbe similarly updated. Thus, customer profiles can be updated both basedon what they watch and on what customers with similar tastes watch.These modified customer profiles would be used for determining virtualchannels and for scheduling which movies to broadcast.

As noted above, the purpose of clustering is to group objects with highsimilarity into clusters. In a multi-channel cable television system,individual channels are often devoted to their specific “audience”, orto a group of customers who enjoy relatively homogeneous preferenceprofiles. Prior to the design of the features of the channels, it isthus necessary to recognize these customer groups as well as theircollective profiles.

There are three basic approaches towards clustering in accordance withthe invention: hierarchical methods, clumping techniques andoptimization techniques. Hierarchical methods fall further into twotypes—divisive or agglomerative. In clumping techniques, some objectsmay belong to several groups simultaneously. Optimization techniques,such as the k-means algorithm and the algorithm for the p-medianproblem, takes the form of linear programming in an iterative approach.

The two algorithms presently preferred for cable television applicationsare: a hierarchical method based on the theory of fuzzy set, with whichthe compactness of the population is estimated, and a revised p-medianmethod, which does the real clustering. A membership equation may alsobe borrowed from the fuzzy logic based k-means algorithm to findcorrespondence of various categories of video programs to the customerclusters.

The process of clustering customers in accordance with the invention iscomposed of three phases:

1) Estimating the distribution of the customer population;

2) Clustering the customer population; and

3) Determining the correspondence of the video program categories to thecustomer clusters.

Estimating the distribution of the customer population is necessarybecause the value of an adjustment parameter used to cluster thecustomer population depends on the compactness of the population.Knowing the distribution of the customer population also helps one tomake general judgments on the validity of the number of clustersobtained in the last phase.

The fuzzy logic based hierarchical method mentioned above is preferablyused for the estimation of population compactness since the theory offuzzy set is suitable for dealing with uncertainty and complex phenomenathat resist analysis by classical methods based on either bivalent logicor probability theory. As known by those skilled in the art,hierarchical clustering methods generate a hierarchy of partitions bymeans of a successive merging (agglomerative) or splitting (diversion)of clusters. This type of clustering method corresponds to thedetermination of similarity trees where the number of groups q(a)increases monotonously as the value of a increases, where a representsthe degree of “belonging” of an element in a group. A group is broken upwhen the value of α goes beyond the minimum membership value within thatgroup. Therefore, loosely formed groups, typically found in a scatteredpopulation, break up at low levels of α, while highly dense groups,often seen in a compact population, dismantle only at high levels of α.Consequently, in a graph with α on the x axis and q on the y axis, theformer yields a concave curve and the latter gives a convex curve.

The compactness of a population may be established using the followingequation, where C is a measure of compactness: $\begin{matrix}{C = {\int_{0}^{1}\frac{\alpha\quad{q(\alpha)}{\mathbb{d}\alpha}}{q(1)}}} & {{Equation}\quad(27)}\end{matrix}$This equation, of course, is difficult to calculate. It is thuspreferable to use its discrete version: $\begin{matrix}{C = {\beta*\frac{\sum\limits_{\alpha = \alpha_{0}}^{\alpha_{n}}{h_{\alpha}{q(\alpha)}}}{q\left( \alpha_{n} \right)}}} & {{Equation}\quad(28)}\end{matrix}$where h_(α)=α_(i)−α_(i-1), the interval of α. A typical setting isα₀=0.1, α_(n)=1.0, and h_(w)=0.1.

For the membership function of the similarity relation, μ_(R)(i,j),which indicates the similarity between customers i and j, and anagreement scalar, ac_(ij), similar to the one defined above can be used.This time ac_(ij) is defined as:ac _(ij)=1/[1+(1/K)Σ_(k)(wv _(ik) /W _(i))t _(ijk]) (i=1, 2, . . . N;j=1, 2, . . . N)  Equation (29)where: $\begin{matrix}{\begin{matrix}{t_{ijk} = {{\left( {c_{ik} - c_{jk}} \right)}/s_{{cik} - {cjk}}}} & {{{if}\quad c_{ik}} \geq 0} \\{= \infty} & {{{if}\quad c_{ik}} = {- 1}}\end{matrix}\left( {{i = 1},2,\ldots\quad,{N;{j = 1}},2,\ldots\quad,{N;{k = 1}},2,\ldots\quad,N} \right)} & {{Equation}\quad(30)}\end{matrix}$and:S _(Cik-Cjk)=√{square root over ((s _(ik) ² +s _(jk)²)/(N−1))}  Equation (31)where:ac_(ij) is the agreement scalar between the profiles of customeri and the profiles of customer j;t_(ijk) is the t value for significance of the difference between therating of characteristic k by customer i and by customer j;c_(ik) is customer i's rating for characteristic k;s_(jk) is the spread (flexibility) in customer i's rating forcharacteristic k;s_(Cik-Cjk) is the standard deviation between the distribution of C_(ik)and that of C_(jk);wv_(ik) is customer i's weight of characteristic k;W_(i) is Σ_(k) wv_(ik), i.e., the sum of all weights for customer i; andN is the sample size.

One is added to the denominator in Equation (29), so that 0<ac≦1, whichis the range required for a valid membership value.

The actual clustering is done by a revised p-median algorithm.Traditional p-median clustering algorithms require the prior knowledgeof p, the number of clusters, a requirement often difficult to meet inreality, especially in an application of the type described here.Accordingly, a modified p-median clustering algorithm is preferably usedwhich introduces p into the objective function, thereby eliminating theiterative nature of the algorithm and overcoming the difficulty ofguessing an initial p value.

In the population clustering method in accordance with the invention,the grouping of objects moves in the direction of minimizingdissimilarity between the objects, where dissimilarity between objectsis indicated by some measure of “distance” between them. In other words,for a population V that contains N customers, any customer can bedescribed in the system by a vector of K characteristics. Thus, for anytwo customers i and j:V _(i=[c) _(i1),c_(i2), . . . , c_(ik), . . . . , c_(iK)]V_(j)=[c_(j1), c_(j2), . . . , c_(jk), . . . , c_(jK)]which in fact are the preference profiles of the two customers, whichare determined as set forth in detail above. In general, the distancebetween the two customers i and j is defined as:d _(ij)=Σ_(k=1) ^(k) f _(k)(c _(ik) ,c _(jk)),  Equation (32)where f_(k)(c_(ik),c_(jk)) is the individual measure of distance foreach characteristic. The distance may be determined in several ways suchas absolute “City Block” distance, Hamming distance, Euclidean distance,etc. A major drawback of using these measures in the present invention,however, is that they fail to recognize the spread (flexibility) in acustomer's rating for the characteristics. In order to take theflexibility into consideration, it is necessary to define:d _(ij)=[Σ_(k)(w _(ik) /W _(i))t _(ijk) ]/K (i=1, 2, . . . N, j=1, 2, .. . N),  Equation (33)where w_(ik), W_(i), and t_(ijk) are defined as above. Distance d_(ij)is somewhat like the reciprocal of the agreement scalar sc_(ij).

In order to incorporate p, the number of clusters, into the objectivefunction so as to optimize p, its coefficient should reflect the natureof the entire population. The global mean used in the formulation of theproblem is thus: $\begin{matrix}\frac{\sqrt{\sum\limits_{i}{\sum\limits_{j}d_{ij}^{2}}}}{n^{2}} & {{Equation}\quad(34)}\end{matrix}$where d_(ij)≧0, for i,j, i≢j; and d_(ij)=0, for i,j, i=j. The objectivefunction is the minimization of the total sum of the distance betweenthe customers for the case where each customer is assigned to exactlyone cluster.

As noted above, a (0.5≦α≦1.5) is an adjustment parameter. For normalpopulations, its value is 1. For abnormally distributed populations,however, its value may change. For instance, in a highly scatteredpopulation, the value of α may increase so as to create highlydistinguished clusters. Therefore, α is a negative function of C, thecompactness measure defined in Equations (27) and (28) above. A possibleform of the function could be α=β/C, where β is a parameter whose valueis determined through is calibration.

The designing of a “virtual” video channel, which is oriented towardsone or more customer groups, should be performed so that the combinedfeatures of the program categories it carries match the preferences ofits target customers. It is therefore important to know thecorrespondence of various video categories to the customer clustersobtained after running the clustering algorithm set forth above. One wayto determine the correspondence between a program category and acustomer cluster is to calculate the “membership” of the category in thecluster. The “membership” function for Category l in cluster i isdefined as: $\begin{matrix}{\mu_{i\quad 1} = \frac{\left( \frac{1}{d_{i\quad 1}} \right)^{M}}{\sum\limits_{j}\left( \frac{1}{d_{j\quad 1}} \right)^{M}}} & {{Equation}\quad(35)}\end{matrix}$where d_(i1) is defined as in Equation (33). M is a weighting parameter(M>1), which reduces the influence of small μ_(i1) compared to that oflarge μ_(i1)'s (customer clusters close to the category). The more M isgreater than one, the more the reduction.

D. Creating Initial Profiles from Clusters

As noted in Section II.B. above, there are several methods fordetermining initial customer and content profiles. For example, initialcustomer profiles may be established by having the customer select a fewof his or her favorite movies or television shows and then using thecontent profiles of those movies or shows to construct a customerprofile. In addition, the initial customer profile may be based onreplies to questions asked of the customer, or conversely, the customermay be assigned a customer profile typical of people in his or herdemographic group. Similarly, initial content profiles may beestablished by using ratings by experts or test groups indicating thedegree of presence of different characteristics or by using the relativefrequencies of words in movie reviews or closed captioned listings andthe like. However, it is often useful to use data indicating whichprograms each viewer has watched in order to determine the initialprofiles for either new customers or new programming.

Intuitively, the customer profiles of new customers should look like thecontent profiles of the movies and/or shows they watch, and the contentprofiles of new movies should look like the customer profiles of thecustomers who watch those movies. If each customer has a single customerprofile, the method for determining the customer profile is simple: onesimply finds the centroid of the content profiles of all the moviesand/or shows watched by the customer. However, since each customer mayhave multiple customer profiles, only one of which is expected to matcheach movie or show, the movies watched by a customer must be clusteredinto groups for selection of the centroid (average) of each group.Similarly, if one has a list of people who have watched a movie or show,one can determine a content profile for that movie or show by clusteringthe profiles of the customers and selecting the profile clustercontaining the most customers.

By using clustering techniques, one can also determine an initialcustomer profile even if no history of the customer's preferences isavailable. In particular, by clustering customers based on demographicor psychographic data, new customers may be assigned customer profilestypical of customers with similar demographics or psychographics. On theother hand, when no characteristics are known for movies or customers,an agreement matrix indicating which movies each customer is likely towatch may be computed from a record of which movies each customer hasalready watched. As described above, this agreement matrix can be usedfor selecting a set of virtual channels for each customer, forscheduling movies for delivery over a cable or equivalent transmissionsystem, and for making movie rental or other rental or purchaserecommendations at a kiosk or personal computer (described below). Thekey to generating the agreement matrix using this approach is theobservation that if two people have liked many of the same movies orshows in the past, then they are likely to continue to like similarmovies or shows. More precisely, if a person “A” has seen and liked manymovies or shows which a second person “B” has seen and liked, then “A”is likely to like other movies or shows which “B” liked. The method setforth below generalizes this concept to multiple customers.

In the simplest use of clustering, a record is kept of all movies orshows watched by all customers. If the customers are not identified,they are identified by whether or not their television is on. Thecustomers are then grouped so that people who have watched more moviesor shows in common are more likely to be in the same group. In otherwords, the customers are divided into groups to minimize the sum overall the groups of the sum over all pairs of group members of thedistance between the members. Practically, this means that the distancefrom the centroid of the group is computed since it is cheaper tocompute. Since the inverse of the distance is a measure of agreement,the clusters are preferably selected to maximize agreement among thecluster members.

Once the customers have been clustered into groups, the effectivepopularity of movies or shows for the cluster can be determined bycounting the total number of times each movie or show was watched. Anagreement matrix between the customers and movies or shows may beconstructed based on these clusters by assigning each customer theagreements (“effective popularity”) of the movies or shows for thecluster that the customer is in, where all members of a group have thesame agreement.

In particular, a technique for creating initial profiles from clusterdata includes the steps of:

(1) picking the number of desired groups, K;

(2) using the k-means algorithm to group the customers into K groups tominimize the sum over all the groups of the sum over all pairs of groupmembers of the distance between each group member and the groupcentroid. In other words, it is desired to minimize: $\begin{matrix}{\sum\limits_{\underset{k = {1\quad{to}\quad K}}{clusters}}{\sum\limits_{\underset{i\quad i\quad n\quad k}{customers}}{{v_{i} - v_{k}}}}} & {{Equation}\quad(36)}\end{matrix}$where |v_(i)−v_(k)| is the distance between the vector of movies watchedby customer i and the centroid of cluster k. v_(i) is one for each moviewatched by customer i and zero for each movie not watched. The simplestmeasure is the Euclidean distance: $\begin{matrix}{{{v_{i} - v_{k}}} = {{sqrt}\left( {\Sigma_{m}\left( {v_{im} - v_{k\quad m}} \right)}^{2} \right)}} & {{Equation}\quad(37)}\end{matrix}$where v_(im) is the value corresponding to the mth movie watched (or notwatched) by customer i; and

(3) determining the agreement matrix elements ac_(ij) For each customeri, the jth row of the agreement matrix is just the vector v_(k) for thecluster k that the viewer i is in.

As an example of this technique, assume the following viewing history,where each “x” indicates that a video program (A-G) was watched by acustomer (1-6): program customer A B C D E F G 1 x x x x x 2 x x x 3 x xx 4 x x x x 5 x x x 6 x x x x

Clustering using a standard algorithm such as k-means clustering willdivide the above people into two groups: {1,2,3} and {4,5,6}. Thecentroid of each cluster is found by calculating the average number ofviewers of each movie in the cluster. The centroid of the {1,2,3} groupis {2/3,2/3,1,1,1/3,0,0}, corresponding to movies A, B, C, D, E, F, andG, respectively. The centroid of the {4,5,6} group is{0,0,1/3,1,2/3,1,2/3}. The resulting agreement matrix is thus: programcustomer A B C D E F G 1 2/3 2/3 1 1 1/3 0 0 2 2/3 2/3 1 1 1/3 0 0 3 2/32/3 1 1 1/3 0 0 4 0 0 1/3 1 2/3 1 2/3 5 0 0 1/3 1 2/3 1 2/3 6 0 0 1/3 12/3 1 2/3In a broadcast/cable application of the type described herein, it may bedesirable to construct different agreement matrices of this type fordifferent times of day or days of the week.

This technique can be refined in two ways: (1) by using fuzzy clusteringtechniques, where a customer may belong to different clusters, and (2)by requesting a rating from each customer for each movie viewed. In thecase of fuzzy clustering, each customer gets an agreement matrix whichis the sum of the agreement matrices for the groups he or she belongs toweighted by the degree to which the viewer belongs to the group. In thecase of rating requests, on the other hand, the clusters are made notjust based on whether the movies were watched, but also based on howmuch they were liked as determined from the viewers' ratings of themovie (for example, on a scale of 1 to 10). In the latter case, adifferent distance metric should be used so that an unrated movie is notconfused with a movie that was viewed and not liked. An appropriatemetric is to use the Euclidean distance but to exclude all programs notreviewed by the customer. A preferred embodiment for kiosks or personalcomputers (described below) incorporates both of these refinements.

One skilled in the art will recognize that many additional variations onthis technique are possible within the scope of the invention. Forexample, instead of a standard Euclidean distance metric, one may wishto use the inverse of the fraction of movies which were watched by bothmembers of the pair. As another alternative, agreements can benormalized by the number of movies or shows the customer has seen. Also,customers who do not want to watch movies repeatedly may block theviewing of recently viewed movies to avoid repeat viewing.

VI. Hardware Implementation of Profile System

Two hardware embodiments of the invention may be used to implement thesystem described above. In a so-called one-way data transmission system,no feedback from the set top multimedia terminal is provided foradjusting the customer profiles or the content profiles. In a two-waydata transmission system, on the other hand, passive feedback techniquesare used to better personalize the video offerings over time. Bothhardware embodiments will be described below.

A. One-Way Data Transmission System

In a one-way data transmission system in accordance with the invention,a customer profile system in accordance with the invention calculatesthe agreement matrix at the customer's set top multimedia terminal fromthe customer profiles stored in the set top multimedia terminal and thecontent profiles of the received video programming. This techniqueallows the set top multimedia terminal to create “virtual channels” ofthe video programming received which the set top multimedia terminaldeems most desirable on the basis of the customer's profile(s).

The first embodiment thus does not use any of the feedback and updatingtechniques described above. FIG. 4 illustrates a generalized diagram ofsuch a one-way video distribution system in accordance with the firsthardware implementation of the invention. As illustrated in FIG. 4, aplurality of program source materials 402 are modulated by a pluralityof channel modulators 404 and distributed via distribution system 406 athead end 408 and via respective nodes 410 to set top multimediaterminals 412 in the homes of the head end's customers. In thisembodiment of the invention, the set top multimedia terminals 412 and/orthe distribution system 406 include software such as that describedabove for determining an agreement matrix for each customer. Theagreement matrix suggests programming for “virtual channels” and/orcontrols the tuners of the set top multimedia terminals 412 to selectthe most desired programming for the customers in accordance with thecustomer's profiles. In other words, a plurality of “virtual” channelsare created from the agreement matrix, and the selected programming isprovided from each set top multimedia terminal 412 to the associatedtelevision. The customer then decides whether he or she wants to watchone of the “virtual” channels or one of the conventional channels.

In the embodiment illustrated in FIG. 4, the set top multimediaterminals 412 sit on top of the television and receive as input theshows being broadcast and their associated content profiles (either inthe bit stream, the vertical blanking interval, or separately as part ofthe electronic program guide information). The set top multimediaterminals 412 have the customer profiles for that residence prestoredtherein. Set top multimedia terminal 412 may also include means formonitoring which shows are being watched by the customer. From thisinformation, the customer profiles stored in the set top multimediaterminal 412 may be modified by the software of the set top multimediaterminal 412 using the techniques described in Section II.B. above. Inother words, each set top multimedia terminal 412 preferably includesmeans for updating the customer profiles based on what the customeractually watched. However, the set top multimedia terminals 412 do notprovide the list of the watched programs back to the head end foradjusting the video programming schedule since a two-way datatransmission system would be required.

B. Two-Way Implementation

The second embodiment of the invention incorporates the above-mentionedpassive feedback techniques to provide information from the set topmultimedia terminals back to the head end so that the video programmingschedule may be adjusted and so that targeted advertising and the likemay be provided from the head end. This embodiment differs from thefirst embodiment in that data regarding the customer's selections ofprogramming is collected by the head end for use in future programscheduling. Data collection in accordance with the invention is theprocess by which the customer viewing results and/or profiles arecollected by the CATV and/or conventional broadcast system forsubsequent processing and assimilation. In the two-way implementation,the customer profile system is implemented at the video head end bycreating an agreement matrix for all customers from customer profilesstored at the head end and content profiles of the video programming tobe transmitted. This technique allows the video head end operator toobjectively determine which video programming is most likely to bedesired by his or her customers and also allows one to minimize thememory requirements at the set top multimedia terminal.

Two main hardware implementations for data collection are describedherein with reference to the preferred two-way embodiment: telephonesystem return and CATV system return. Both of these approaches utilize a“wired” return path for data collection. In addition, those skilled inthe art will appreciate that several wireless alternatives for datacollection are possible. The specific implementation selected dependsupon several variables, including the technology in place on the CATV orconventional over air broadcast system, specific polling techniquesemployed, telephone system flexibility, the required/desired frequencyfor polling the data, and the level of maintenance employed on the CATVor conventional over air broadcast system. Details of a telephone systemimplementation are highlighted in FIGS. 5 and 6.

FIG. 5 illustrates a generalized diagram of a two-way video distributionsystem in accordance with the invention. In this embodiment, thecustomer profile information and viewing habit information from theindividual set top multimedia terminals is relayed to the head end 502on a periodic basis for updating the agreement matrices on a systemlevel to determine what video programs should be transmitted inparticular time slots. As in the one-way embodiment of FIG. 4, programsource material 402 is modulated onto respective channels by modulators404 for distribution to the customers. However, in the two-wayembodiment of FIG. 5, the head end 502 includes a distribution system504 which is controlled by system controller 506 to schedule thepresentation of the program source material 402 to the customers inresponse to passive feedback data stored in data collection memory 508which has been received from the customers' set top multimedia terminals412. In particular, the customer profile data and viewing habit data iscollected and periodically provided via return path 510 to datacollection memory 508 as a record of what the customers desire to watchand what they actually watched.

In accordance with the techniques described in detail above, thisinformation is then used to appropriately update the system profiles(composite of all customer profiles) and/or the content profiles ofvideo programs and thus, in turn, is used in adjusting the scheduling ofthe program source material 402 for transmission via nodes 510 to therespective set top multimedia terminals 512 in the customers' homes. Asin the one-way embodiment of FIG. 4, each set top multimedia terminal412 then determines “virtual” channels for presentation to thecustomers' televisions. As noted above, return path 510 preferablyconstitutes a telephone connection, although the return path 510 couldalso be a portion of the broad band cable connection.

FIG. 6 illustrates an actual cable television distribution system for acable television implementation of the present invention. Asillustrated, a variety of modulated program sources 602 are provided.The programs are selectively (and dynamically) provided to each node viaa dynamic program matrix switch 604 at the cable head end. Also at thehead end is a cable television (CATV) system controller 606 whichdesignates which programs are to be delivered to each node. The videosignal from the switch 604 is amplified by amplifiers 608 and thentransmitted over conventional optical fiber and/or coaxial cables 610 tosplitters 612 and repeater amplifiers 614 for provision to thecustomers' homes via coaxial cables 616 and a tap 618. As described withrespect to FIGS. 4 and 5, each home on the network is equipped with aset top multimedia terminal 620 which calculates the agreement matrixand generates virtual channels in accordance with the techniques of theinvention. If a two-way implementation is used, a data collectionmechanism 622 may also be provided for accepting passive feedback datavia path 624 from the set top multimedia terminal 620, as in theembodiment of FIG. 5.

In the embodiments of FIGS. 5 and 6, the return path from each remotecustomer's multimedia terminal to the data collection mechanism at theCATV head end is preferably provided through the telephone network. Suchtechniques are currently employed in CATV systems for collection of thePay-Per-View purchasing information to ascertain billing by customers.As in those systems, a telephone interface (FIG. 10) is provided at eachcustomer location, which is, in turn, connected to the multimediaterminal's microprocessor to facilitate information transfer between themultimedia terminal's memory and the CATV head end. As will be describedbelow with respect to FIG. 10, the memory of the multimedia terminalincludes relevant profile information and/or specific viewing/purchasingdetail records for any and all customer(s) at that remote customerlocation.

The data collection system (508, 622) can either operate on a real-timeor a non-real-time basis, depending upon the desired/required refreshrate for the data collection. In addition, for telephoneimplementations, any system constraints imposed by the telephone systemitself may effect the data collection periodicity and whether it is inreal-time or not. Such constraints may be necessary to prevent telephonesystem overload, which is more likely to occur if data from all theremote terminals were collected at once.

At the CATV head end, the data collection hardware (508, 622) includes atelephone interface, a memory, and a processor which allows for“polling” of the remote terminals in conjunction with the CATV systemcontroller (506, 606). Upon command from the CATV system controller(506, 606), each remote terminal is instructed to “send back” to thehead end relevant data for central collection and processing. The datais received through a network interface, and in the case of thetelephone network, through the afore-mentioned telephone interface.

The data is then stored in memory of data collection hardware (508, 622)at the CATV head end for processing using the techniques of theinvention. In particular, the CATV system controller (506, 606)processes the data collected to maximize the desirability of theprogramming available on the network. This can be accomplished throughclustering the collected data or through other appropriate means. Oncethe “optimum” desirable programming is determined, the CATV systemcontroller (506, 606) selects, then “routes,” the appropriate sourceprograms through the Dynamic Program Matrix Switch 604 to the CATVnetwork as illustrated in FIG. 6. As the name “dynamic” implies, thecontent and mix of the source programs placed on the network at anygiven time can change as a result of the changing composite of customerprofiles on the network at any given time. In addition, each node of thenetwork can be supplied with its own unique set of independent dynamicsource programs from the Dynamic Program Matrix Switch 604.

Since the data passing from the set top multimedia terminal to the headend contains data which the customers may consider to be confidential,the two-way transmission system may be modified to encrypt thetransmissions from the set top multimedia terminals to the head end.Similarly, as in the case of Pay-Per-View programming, it is oftendesirable to encrypt the transmissions from the head end to the set topmultimedia terminals. Unfortunately, the bandwidth demands oftransmitting digital video and encrypting it in real-time necessitatethat any data stream encryption and decryption be of relatively lowcomputational complexity. Additionally, the system should be safe fromunauthorized interception and decryption. This may be accomplished byusing a one-time session key.

One-time session keys (Vernam Systems) are proven as unbreakable and areof trivial complexity to implement, once the keys are available. Aone-time session key involves generating a cipher key which is the samelength as the message. The encryption occurs by applying the appropriateith entry of the key, K_(i), to the ith symbol in the plain text, P_(i).For example, the cipher text equivalent, C_(i), for P_(i) isK_(i)+P_(i). Since K_(i) is an element of a uniformly distributed randomsequence, it is impossible to solve for P_(i) without knowing K_(i).Since K_(i) is an element of a uniformly distributed random sequence ofthe same length as the message, it removes any possible statistical orstructural information that might be exploited in breaking the code.Encryption and decryption are of moderate complexity since they involvedecrypting C_(i) by P_(i)=C_(i)−K_(i). In Vernam systems, rather thanusing the addition operator, the bit-wise exclusive-OR (EOR) operator isused since it forms the identity operator for even numbers ofapplication (i.e., EOR(K_(i),EOR(K_(i),P_(i)))=P_(i).

The main problem with such methods is in the key distribution, that is,in sharing the one-time session key between the originator and therecipient. In the case of head end to set top multimedia terminalcommunications, the following simple solution is proposed. Instead of aone-time session key, a seeded pseudo-random number generator is used togenerate a sequence of random numbers. The seed for the generatordetermines the infinite sequence of random numbers, which, in turn,forms the one-time session key. For a given initial seed, the entirepseudo-random sequence may be regenerated. For example, twopseudo-random number generators (e.g., the Linear CongruentialAlgorithm) using the same seed will generate the same pseudo-randomsequence. The seed is encrypted using a high level of encryption such asthe RSA public key algorithm with long bit length public and privatekeys. If the seed is unknown to third parties, and the random numbergenerator is sufficiently unbiased and noninvertible, then it will beimpossible for an unauthorized third party to determine the sequence ofnumbers forming the one-time session key. If both the sender andreceiver are synchronized and utilize the same initial seed, they willhave the same one-time session key, and thus will be able toconsistently encrypt and decrypt the messages. Thus, instead of sendingthe long key, a single encrypted initializer is sent. The system isunbreakable to the extent that the public key system (RSA) isunbreakable, but the computational simplicity of the one-time sessionkey allows it to be implementable in hardware for very fast encryptionand decryption at the head end and at the set top multimedia terminal.

Thus, as shown in FIG. 7, upstream encryption for a secure transmissionpath for transmitting preference data, profile data and the like fromthe set top multimedia terminal to the head end is performed as follows:

(1) At the head end, generate a seed random number N to be used for therandom number generator (step 702).

(2) Retrieve the public key P from the set top multimedia terminal (step704) and encrypt the seed random number N as E(N,P) at the head endusing a public key algorithm such as RSA which is known to be difficultto break (step 706).

(3) Send the encrypted seed N (E(N,P)) to the set top multimediaterminal (step 708) where E(N,P) is received (step 710) and decrypted toyield N using the set top multimedia terminal's private key Q (step712).

(4) The head end and set top multimedia terminals then initialize theirrespective pseudo-random number generators with N as a seed (step 714).

(5) Begin the encryption at the set top multimedia terminal (step 716)by having the encryptor generate the first number in the sequence K_(i)and logically exclusive-ORing it with the first data word in the streamP_(i), thereby forming C_(i) (i.e., C_(i)=EOR(K_(i),P_(i))).

(6) Send the result C_(i) from the encryptor at the set top multimediaterminal to the head end (step 718), where it is received by the headend (step 720).

(7) Form K_(i) at the synchronized random number generator of the headend, which has also been initialized with N, by decrypting the receivedC_(i) to yield P_(i). This is done by exclusive-ORing K_(i) with C_(i)to yield P_(i) (i.e., P_(i)=EOR(K_(i),C_(i))) (step 722), generating thenext pseudo-random K_(i) in the sequence at the head end and the set topmultimedia terminal (step 724), determining whether all words i in thesequence have been decrypted (step 726), and repeating steps 716-726until all words in the digital video stream have been decrypted. Normalprocessing of the digital video stream continues from that point (step728).

As illustrated in FIG. 8, for encryption of the video programming datatransmitted from the head end to the set top multimedia terminals, theprocedure is identical to steps (1)-(7) above illustrated in FIG. 7,except that the roles of the head end and set top multimedia terminalare reversed.

Advantages of such an encryption/decryption technique include the factthat the operations for encryption and decryption include only anexclusive-OR, which is a one gate delay logical operation. Also, manyrandom number algorithms may be implemented which execute rapidly inhardware shift/divide/accumulate registers. Accordingly, it is desirableto use such an encryption/decryption technique to maintain the securityof the two-way data transmission system described in this section.

C. Set Top Multimedia Terminal Embodiments

FIG. 9 illustrates a software block diagram of an embodiment of amultimedia terminal 620 for use in the one-way and two-way systemembodiments described above. As illustrated, the video program materialand the associated content profiles are received at the set topmultimedia terminal 620 from the head end 408. A program list indicatingthose video programs which the user of that set top multimedia terminal412 has available and is authorized to receive is stored in memory 902.The associated content profiles (program characteristic lists) ispreferably received with the electronic program guide data and stored inmemory 904. From the content profiles stored in memory 904, processor906 calculates and updates the agreement matrix using the techniquesdescribed in detail above and stores the resulting agreement matrix inmemory 908. As noted above, the customer profiles used in calculatingthe agreement matrix preferably differ in accordance with the time ofthe day and of the week and/or the expected mood of the customer.Accordingly, a record of the time of day is stored in memory 910 asreceived separately from the CATV head end or as input by the customerand maintained locally at the set top multimedia terminal 620.Similarly, the expected mood of the customer is stored in memory 910. Asdesired, the expected mood may be accessed and modified by the customer.

From the agreement matrix determined by processor 906 and stored inmemory 908, a list of “preferred channel selections” or “virtualchannels” is determined. An electronic program or display guide 914listing the available selections is provided. In accordance with theinvention, the display guide 914 is either modified to include fieldsfor the “virtual” channels, or else the recommended programming ishighlighted in an obvious manner or reordered for the customer's perusaland selection of the desired programming. Once the customer has selectedthe desired virtual channel from a highlighted program guide or alisting of the programs available on the virtual channels using thecustomer's remote control unit, processor 906 then accordingly instructschannel selector 912 to tune the channels for the programming determinedin accordance with the techniques of the invention to be most desirableto that customer. Display guide 914 also permits the customer to viewhis or her stored customer profiles including the characteristics andthe associated weighted values. This allows the customer to manuallymodify his or her customer profiles while they are displayed on thescreen and/or to select one or more categories to which a selectedprofile is relevant.

As noted above, numerous customer profiles may be stored at each set topmultimedia terminal, each corresponding to a different customer and/ormood of the customer or customers. It is thus desirable that thecustomer 916 be provided with a customer identifier interface 918, suchas a remote control or keypad unit, through which the customer canspecify which customer profile to use at a given time and hence whichagreement matrix is relevant. In other words, the customer identifierfunctional block 918 may be used to differentiate multiple customers orto override the mood indicator 910 to allow the customer to select adifferent profile than that which would otherwise be recommended inaccordance with the time of day or expected mood of the customer. Thecustomer identifier functional block 918 may also allow a customer tolock out others from using a particular profile for a particular virtualchannel, such as an “adult” channel which the customer would not likehis or her children to view. The customer identifier functional block918 may further allow the customer to manually change and/or modify hisor her customer profiles by adjusting the weights or values of certaincharacteristics. Also, manual adjustment may be used to allow parents toset profiles for their children and/or to limit the children's access tothe parents' profiles. In this manner, parents will be given morecontrol to limit what their children watch to educational or othersuitable programming, even when the parents are not present to supervisethe children's viewing habits. For this purpose, it is desirable thatdisplay guide 914 be permitted to display the customer profiles andweightings from agreement matrix 908 and the program list from memory904.

The software illustrated in FIG. 9 is stored in the set top multimediaterminal 620 connected to each customer's television. A currentlypreferred hardware embodiment of the set top multimedia terminal willnow be described with respect to FIG. 10.

FIG. 10 illustrates a hardware embodiment of set top multimedia terminal620. As shown, the video program material and corresponding contentprofiles are received from the head end 502 by tuner 1002, or thecontent profiles are separately received at data receiver 1004 alongwith the electronic program guide information via the dotted line path.If scrambling is employed, as in the transmission of Pay-Per-View videoprogramming, the scrambled video signals are supplied from tuner 1002 todescrambler 1016 before being further processed by microprocessor 1006and/or modulated by modulator 1018 for display in accordance with theinvention. If tuner 1002 selects a channel containing video program datain its vertical blanking interval (“VBI data”) received from head end502, the VBI data is supplied is directly to microprocessor 1006 and/orthe content profile data is supplied to microprocessor 1006 via datareceiver 1004. The video data is supplied directly to the descrambler,as necessary, and then to the modulator 1018 for display in aconventional manner.

Microprocessor 1006 generates the agreement matrix as described indetail above. Input from the customer is provided to microprocessor 1006via remote control device 1008 and infrared receiver 1010 associatedwith the set top multimedia terminal 620. The customer profile dataand/or records of the viewing habits of the customer are stored inmemory 1012 and used in the calculation of the agreement matrix bymicroprocessor 1006. From the agreement matrix, microprocessor 1006satisfies the customer's “appetite” for video programming by creating adesignated number of “virtual” channels for the customer's considerationat any given time. The “virtual” channels determined by microprocessor1006 are then presented to the customer's television via screengenerating circuit 1014 and a modulator 1018 in accordance with knowntechniques. The customer then tunes to the desired channel or “virtual”channel to receive the program selected to match that customer'sinterests. Power for the illustrated circuitry is provided by powersupply 1019.

For use in the two-way system described above with respect to FIG. 5,the set top multimedia terminal 620 of FIG. 10 is modified to includethe features indicated in phantom. In particular, telephone interface1020 provides a reverse path for collecting the customer profile andviewing habit data from memory 1012 in a database at the head end 502 ona periodic basis. As noted above, this information is preferablyencrypted by encryptor 1022 before transmission to the head end 502 forappropriately updating the customer profiles and the content profilesand modifying the scheduling of video programming to all of thecustomers serviced by that head end 502. Alternatively, an RF modulator1024 may be provided for providing real-time communication directlybetween the set top multimedia terminal 512 and the head end 502 via theCATV or over air transmission system.

Of course, other set top multimedia terminal designs are possible inaccordance with the invention. For example, if the agreement matrix foreach customer is calculated at the video head end, the electronics ofthe set top multimedia terminal 620 are greatly simplified. In addition,appropriate modifications can be made to the circuitry for use in thevideo head end. Such modifications are believed to be readily apparentto those skilled in the art.

VII. Alternative Embodiments of Systems which Use Agreement Matrix

While a preferred embodiment of the invention has been described withrespect to a video distribution system, the present invention may beused to selectively provide other materials such as news, video games,software, music, books and the like to customers based upon the profilesof those customers. The present invention also may be modified for usein an interactive system to anticipate what customers are likely torequest so that the information may be downloaded in advance using, forexample, a simple Markov model and/or probability transition matrices inan event graph. The present inventors contemplate many such embodimentswithin the scope of the claims and will highlight a few such embodimentsbelow. Of course, many other embodiments within the scope of the claimswill become apparent to those skilled in the art.

A. Video Distribution Systems

As described in detail above, a preferred embodiment of the inventiondetermines an agreement matrix for matching customer preferences toavailable video programming and presenting the most desirable videoprograms on one or more “virtual channels” customized for the customer,thereby minimizing “channel surfing”. This is accomplished bycalculating an agreement matrix which matches the characteristicsdesired by customers with corresponding characteristics of the videoprograms. In one alternate embodiment of the invention described above,video programs that tend to be liked by the same people are clusteredtogether or, on the other hand, customers with similar interests areclustered together using the agreement matrix. In this manner, thesystem of the invention is used to determine which video programmingbest meets the needs of a designated viewership.

It has also been suggested above that clustering techniques may be usedto provide a relatively homogeneous population with targetedadvertising. What is significant about the invention in this context isthat the agreement matrix may be updated based on feedback includingactual purchases made by the customer in response to such targetedadvertising. For example, when shopping at home using infomercials, aswhen watching a movie, the products available for purchase can becharacterized using different attributes and an agreement matrix formedbetween customer profiles and product profiles. The agreement matrix canalso be used to select infomercials or other advertisements that thecustomer is most likely to watch and to respond to by making purchases.If purchase information is available, the customer profiles can beupdated using the same algorithm described above with respect to videoprograms, but now the updating is based on what the customer actuallypurchased as well as what infomercials he or she watched.

The clustering method of the invention may also be modified to includesociodemographic profiles of customers. Such information may includeages, gender, and race, as well as other information provided by thecustomers themselves. On the other hand, the clustering data may includecensus data such as zip code data. For example, as noted above, a zipcode may be used as one way to categorize the customer profiles of thecustomers whereby a new customer to a system would get one or more of anumber of generic customer profiles for a particular zip code as his orher initial customer profile. The initial customer profile would then bemodified as that customer's viewing habits are established. As notedabove, such modifications may be accomplished using psychographic data,customer preference profiles input directly by the customer, past movieselections, rave reviews, passive feedback based on actual televisionviewing by that customer, records of customer purchases, and the like.

It will also be appreciated that the one-way and two-way systems maycoexist in a hybrid system. In such a hybrid system, the feedback pathsfrom the two-way set top multimedia terminals could be used by testaudiences to provide initial content profiles for new movies before themovies are made available to all. Similarly, the feedback paths from thetwo-way set top multimedia terminals may be used to provide initialcontent profiles for subsequent episodes of television series. By usingthis approach, no experts or studio test groups would be needed toestablish the initial content profiles for new video programming. Also,content profiles would be available for all video programming other thanthat provided solely to the test audiences.

Generally, the two-way set top multimedia terminals would belong tocustomers connected to the same nodes as other customers having one-wayset top multimedia terminals. As a result, the content profilesdetermined from the test programming and the like may also be used toprovide initial customer profiles specific to a new customer to thatnode. Such a technique may also be used to monitor changing preferencesand even changes in demographics for the customers connected to eachnode by periodically updating the clustered customer profiles for thatnode to reflect the changes in the customer profiles of those customersconnected to a particular node.

Those skilled in the art will also appreciate that the invention may beused in the context of a “Home Video Club” of the type described by Herzet al. in U.S. Pat. No. 5,351,075 to schedule desired programming. Inaddition, the invention may be used as a navigational aid to helpcustomers determine what they want to watch as well as to target a setof movie previews for particular customers to examine.

Those skilled in the art will also appreciate that the basic agreementmatrix described above can be generalized to include various weightingssuch as national popularity, customer requests for movies, customerrequests for times, data on viewership by category and time, and thelike. The present invention is also flexible enough to allow thescheduler to keep regular shows at regular times to draw customers whilegiving the customers the options to select the “best” of what isavailable on the other channels. In such a scenario, one could mixnetwork television with special cable programming as well as video ondemand. Of course, each customer could also have one or more of his/herown “customized” virtual channels showing his or her own requests.Similarly, each customer could adopt the customer profiles of otherindividuals or programs such as “celebrity” profiles including theviewing preferences of different celebrities. However, such “celebrity”profiles must not be updated through passive feedback as describedherein and should remain unchanged.

Also, since there is usually more than one television viewer in ahousehold, it may be desirable to keep multiple clusters of preferencesfor one television. Those skilled in the art will appreciate that thismay be handled in a manner similar to the different moods describedabove. For example, the customer profiles of two or more customers maybe combined, with equal or unequal weightings, so that the videoprogramming with content profiles strictly within the overlap area ofthe combined customer profiles will be preferred. In this manner,customers such as a husband and wife with very different preferences maybe presented video programming options which are mutually agreeable.

Also, the techniques described above may be used to create a virtualchannel for video previews whereby previews of movies and the likeavailable in an on-demand system, for example, may be presented tocustomers in a personalized manner. This may be accomplished even inhotels and the like by providing individuals with personalized ID cardswhich store their profiles and card readers at the set top multimediaterminals which read in the customer's profiles from the ID cards forlocal recreation of the customer's agreement matrix. If desired, theupdated customer profiles may be stored back to the ID card at the endof the customer's television viewing.

B. Video, Music and Bookstore Kiosks

The methods of the invention also may be implemented in a kiosk orpersonal computer as illustrated in FIG. 11 for use in a video, musicand/or book store to help customers decide which videos to rent or musicand books to buy. The kiosk or personal computer would be similar instructure to the kiosk disclosed in U.S. Pat. No. 5,237,157 to Kaplanand would include a microprocessor 1102. However, a kiosk or personalcomputer implemented in accordance with the invention also acceptsidentity information from the customers either via keyboard 1104 or byelectronic reading of a membership card by an electronic card reader(not shown) and retrieves customer profiles for that customer frommemory 1106 for use in forming an agreement matrix as described above.Those skilled in the art will appreciate that, unlike the broadcastembodiment above, it is necessary in the kiosk embodiment to match thecustomer profiles to individuals by name or user ID rather than timeslot. Such values are provided via keyboard 1104 or an electronic cardreader so that the customer profiles for that customer may be retrieved.

Recommendations are then selected by microprocessor 1102 using the samealgorithm described above for the selection of “virtual” channels.Movies which were recently rented by the customer could be determined bychecking that customer's rental records and optionally be removed fromthe list presented to the customer. Customer profiles also would beupdated based on the movies selected using the algorithm and optionallycould be altered to include a rating of the movie provided by thecustomer when he or she returns the video.

The profiling technique of the invention also forms the basis for acustomer to select a movie by example, as in a “rave review” describedabove. As described in Section V.B. above, since customers often do nothave existing profiles, new customers may create an initial customerprofile by selecting one or movies which are similar to what he or sheis looking for so that the profiles of these sample movies may be lookedup and averaged to provide a customer profile. This customer profile isused in combination with a standard set of weights to establish theimportance of the characteristics to generate an agreement matrixindicating how much the customer should like each movie which is nowavailable. The 3 to 5 movies (or 10 movies) with the highest agreement(maximum value for ac) are then presented to the customer via videoprocessor 1108 for display on display device 1110 along with briefdescriptions. As above, movies can be excluded which the customer hasrecently rented. As shown in FIG. 11, a CD ROM player 1112 may also beprovided at the kiosk to facilitate the playing of short “clips” of themovies with the highest agreement to further assist the customer in hisor her final selection.

Another interesting aspect of kiosk embodiments in which user IDs areused to select the customer profiles is that the system may be used tofacilitate the selection of videos which will appeal to several people.For example, the customer may enter the user IDs for those individualsexpected to watch a particular movie rental. The customer profiles foreach person are retrieved and compared to the customer profiles of theothers entered by the customer. The intersection or averaging of thecustomer profiles may then be used in determining the agreement matrixso that the system will select those videos with the most appeal to allpersons specified by the customer.

Alternatively, when an agreement matrix is implemented in a music orbook kiosk to aid in the selection of music or books, thecharacteristics of movies are replaced by the characteristics of musicor books. For music, such characteristics might include standardclassifications such as rock, easy listening, classical, country, orother classifications such as performing artists, decade or century themusic was written, approximate year of release, popularity on “thecharts”, length and the like, while for books, such characteristicsmight include author, standard classifications such as mystery, fiction,non-fiction, as well as length, date of first publication, and the like.Characteristics of the music or books would similarly be matched againstthose desired by customers to create an agreement matrix which woulddirect the customer to those selections most likely to be founddesirable.

Music kiosks and book kiosks could also be used in music and/or bookstores to aid in the selection of music or books for purchase. Unlikethe kiosks described in the Kaplan '157 patent, however, the kioskswould allow potential purchasers to look up music or book selections byexample and would match the customer's preferences to thecharacteristics of the available inventory. The potential buyer couldlisten to segments of those music selections or review the summaries andreviews of those books with the highest agreement to the customerprofile created from the sample music or book selections.

Also, as in the video embodiment described above, the content profilesof certain radio stations may be used to assist the customer inselecting a radio station from those available, or alternatively, a“virtual” radio channel may be created for over air or cabletransmission. Of course, the concepts herein described may be used toschedule music videos and to schedule the transmission of music over airor cable transmission systems. Feedback could also be used to improvethe content and customer profiles as described above with respect tovideo program selection.

C. Data Retrieval Systems

Those skilled in the art will appreciate that the method of generatingagreement matrices for selecting preferred video programming asdescribed herein may be generalized for use in other types of dataretrieval systems besides video and music. For example, the techniquesof the invention may be used for the optimum selection of any chunks ofinformation such as stock market data, print information (e.g., forpersonalized newspapers), or multimedia information which can bedownloaded over networks such as the Internet.

In the case of retrieving stock market data from a computer network,response times for retrieving certain stock market data can be shortenedby anticipating which menu selections the customer is likely to use anddownloading that information in anticipation of its likely use. Oneparticularly useful example of this would be the retrieval ofinformation about stocks such as recent trade prices and volumes. Sincestocks, like movies, can be characterized in multiple ways, such as byindustry, dividend size, risk, cost, where traded, and the like,profiles of stock may be developed in a similar manner to that describedabove. The stocks also can be characterized by whether they are owned bythe customer and by whether they have exhibited unusual recent activity.These characteristics can be used to create profiles and agreementmatrices using the identical techniques described above. In addition, ifa customer exhibits a pattern in their request for information aboutstocks, their requests can be anticipated and menus assembled to easeselection of the stocks so as to avoid potentially long searches throughmultiple windows, or the information can be downloaded in advance of thecustomer's request to reduce waiting time. Such anticipation of customerrequests for information is particularly useful when the waiting timemay be significant, as for multimedia information incorporatinggraphical or auditory information. It is also valuable when largeamounts of information can be transferred at lower cost, for example,using lower cost transmissions at night in anticipation of requests forinformation the following day.

Similarly, in the case of retrieving text or other print information, acustomer may be aided in his or her navigation through a tree ofpossible menu items by having the system anticipate which branches aremost likely to be followed and downloading information in advance of theinformation being requested, thereby significantly speeding up thesystem response. Old information which is unlikely to be used can beflushed from memory. This allows information to be ready at the localmachine when it is needed.

Also, media cross-correlation is also possible using the techniques ofthe invention by using the profile from one media to estimate thecustomer preference for another media. Such an approach might be useful,for example, to predict that an avid customer of sports programs couldalso be very interested in obtaining sports or news information orinformation regarding the purchase of sports memorabilia based on his orher viewing preferences. Likewise, listeners of a particular type ofmusic may also be interested in purchasing concert tickets for the sameor similar types of music.

Finally, the techniques of the invention may be used to match apotential purchaser to real estate on the market by creating profiles ofthe characteristic features of a house such as size, location, costs,number of bedrooms, style, and the like. The potential purchaser canrequest his or her “dream home” by giving example houses, by specifyingdesired characteristics such as range of prices, or by a combination ofthe two. The agreement matrix would match the customer's profiles to theprofiles of the available homes and create an agreement matrix. Thesystem could also verify that the profiles initially entered by thepotential purchasers are accurate by suggesting houses of a somewhatdifferent type than those the customer has requested. A house retrievalsystem which is customer controlled could also be developed using thetechniques of the invention. In this example, the data source would bethe standardized real estate listings.

Although numerous embodiments of the invention and numerous extensionsof the inventive concept have been described above, those skilled in theart will readily appreciate the many additional modifications arepossible in the exemplary embodiments without materially departing fromthe novel teachings and advantages of the invention. Accordingly, allsuch modifications are intended to be included within the scope of thisinvention as defined in the following claims.

1. A method of presenting data from a plurality of data objects, comprising the steps of: creating at least one customer profile for a customer, said customer profile indicating the customer's preferences for data having predetermined characteristics; creating content profiles for each of said data objects, said content profiles indicating at least one of the presence and the degree of content of said predetermined characteristics in data of each of said data objects; relating said at least one customer profile with the content profiles for the data available from each data object; determining a subset of data having content profiles which are determined in said relating step to most closely match said at least one customer profile; and presenting said subset of data to said customer for selection. 2-115. (canceled)
 116. A method of presenting data for selection from a plurality of data sources, comprising the steps of: creating a customer profile for each customer of said plurality of data sources, said customer profile indicating said customer's preferences for predetermined characteristics of the data sources; monitoring which data sources are actually accessed by each customer; and updating each customer profile to reflect the frequency of selection of the data sources by customers with customer profiles similar to said each customer profile.
 117. A method of presenting data for selection from a plurality of data sources, comprising the steps of creating at least one customer profile for each customer, said customer profile including a profile of data previously accessed by said customer; creating content profiles for each data source of said data, said content profiles reflecting the customer profiles of those customers who have previously accessed said data from each data source; relating said at least one customer profile with the content profiles for the data available from each data source to the customer; determining a subset of data having content profiles which are determined in said relating step to most closely match said at least one customer profile; and presenting said subset of data to said customer for selection.
 118. The method of claim 117, wherein the subset of data comprises a menu.
 119. The method of claim 117, wherein the subset of data comprises a tree of possible menu items.
 120. A method of presenting data from a plurality of data objects, comprising the steps of: creating at least one customer profile for a customer, said customer profile indicating the customer's preferences for data having predetermined characteristics; creating content profiles for each of said data objects, said content profiles indicating at least one of the presence or the degree of content of said predetermined characteristics in data of each of said data objects; relating said at least one customer profile with the content profiles for the data available from each data object; determining a subset of data having content profiles which are determined in said relating step to most closely match said at least one customer profile; and presenting said subset of data to said customer for selection.
 121. A method of presenting data from a plurality of data objects, comprising the steps of: creating at least one customer profile for a customer, said customer profile indicating the customer's preferences for data having predetermined characteristics; creating content profiles for each of said data objects, said content profiles indicating the presence of said predetermined characteristics in data of each of said data objects; relating said at least one customer profile with the content profiles for the data available from each data object; determining a subset of data having content profiles which are determined in said relating step to most closely match said at least one customer profile; and presenting said subset of data to said customer for selection.
 122. A method of presenting data from a plurality of data objects, comprising the steps of: creating at least one customer profile for a customer, said customer profile indicating the customer's preferences for data having predetermined characteristics; creating content profiles for each of said data objects, said content profiles indicating the degree of content of said predetermined characteristics in data of each of said data objects; relating said at least one customer profile with the content profiles for the data available from each data object; determining a subset of data having content profiles which are determined in said relating step to most closely match said at least one customer profile; and presenting said subset of data to said customer for selection.
 123. The method of claim 121, wherein said predetermined characteristics comprise at least one of performing artist, year of release, decade or century written, rock, easy listening, classical, country, length, or popularity on the charts.
 124. A method as in claim 121 wherein said and data object is comprised of data representing movies or music.
 125. A method as in claim 121 wherein said predetermined characteristics are comprised of at least one of author, mystery, fiction, non-fiction, length, date of first publication, recent trade price, recent trade volume, industry, dividend size, risk, cost, or where traded.
 126. A method as in claim 121 wherein said data object is comprised of a book, stock market listing, print information, personalized newspapers, real estate listings, or other textual data. 